diff --git a/README.md b/README.md
index 6710a0252..1f56727b4 100644
--- a/README.md
+++ b/README.md
@@ -451,7 +451,7 @@ asyncio.run(main())
 
 ### Choosing Model
 
-By default, PaperQA2 uses OpenAI's `gpt-4o-2024-11-20` model for the
+By default, PaperQA2 uses OpenAI's `gpt-5-2025-08-07` model for the
 `summary_llm`, `llm`, and `agent_llm`.
 Please see the [Settings Cheatsheet](#settings-cheatsheet)
 for more information on these settings.
@@ -895,13 +895,13 @@ will return much faster than the first query and we'll be certain the authors ma
 
 | Setting                                      | Default                                | Description                                                                                                                   |
 | -------------------------------------------- | -------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| `llm`                                        | `"gpt-4o-2024-11-20"`                  | LLM for general use including metadata inference (see Docs.aadd) and answer generation (see Docs.aquery and gen_answer tool). |
+| `llm`                                        | `"gpt-5-2025-08-07"`                   | LLM for general use including metadata inference (see Docs.aadd) and answer generation (see Docs.aquery and gen_answer tool). |
 | `llm_config`                                 | `None`                                 | Optional configuration for `llm`.                                                                                             |
-| `summary_llm`                                | `"gpt-4o-2024-11-20"`                  | LLM for creating contextual summaries (see Docs.aget_evidence and gather_evidence tool).                                      |
+| `summary_llm`                                | `"gpt-5-2025-08-07"`                   | LLM for creating contextual summaries (see Docs.aget_evidence and gather_evidence tool).                                      |
 | `summary_llm_config`                         | `None`                                 | Optional configuration for `summary_llm`.                                                                                     |
 | `embedding`                                  | `"text-embedding-3-small"`             | Embedding model for embedding text chunks when adding papers.                                                                 |
 | `embedding_config`                           | `None`                                 | Optional configuration for `embedding`.                                                                                       |
-| `temperature`                                | `0.0`                                  | Temperature for LLMs.                                                                                                         |
+| `temperature`                                | `1.0`                                  | Temperature for LLMs.                                                                                                         |
 | `batch_size`                                 | `1`                                    | Batch size for calling LLMs.                                                                                                  |
 | `texts_index_mmr_lambda`                     | `1.0`                                  | Lambda for MMR in text index.                                                                                                 |
 | `verbosity`                                  | `0`                                    | Integer verbosity level for logging (0-3). 3 = all LLM/Embeddings calls logged.                                               |
@@ -944,7 +944,7 @@ will return much faster than the first query and we'll be certain the authors ma
 | `prompt.summary_json_system`                 | `summary_json_system_prompt`           | System prompt for JSON summaries.                                                                                             |
 | `prompt.context_outer`                       | `CONTEXT_OUTER_PROMPT`                 | Prompt for how to format all contexts in generate answer.                                                                     |
 | `prompt.context_inner`                       | `CONTEXT_INNER_PROMPT`                 | Prompt for how to format a single context in generate answer. Must contain 'name' and 'text' variables.                       |
-| `agent.agent_llm`                            | `"gpt-4o-2024-11-20"`                  | LLM inside the agent making tool selections.                                                                                  |
+| `agent.agent_llm`                            | `"gpt-5-2025-08-07"`                   | LLM inside the agent making tool selections.                                                                                  |
 | `agent.agent_llm_config`                     | `None`                                 | Optional configuration for `agent_llm`.                                                                                       |
 | `agent.agent_type`                           | `"ToolSelector"`                       | Type of agent to use.                                                                                                         |
 | `agent.agent_config`                         | `None`                                 | Optional kwarg for AGENT constructor.                                                                                         |
diff --git a/src/paperqa/agents/helpers.py b/src/paperqa/agents/helpers.py
index 3d0f48edb..2b3767ced 100644
--- a/src/paperqa/agents/helpers.py
+++ b/src/paperqa/agents/helpers.py
@@ -26,9 +26,9 @@ def get_year(ts: datetime | None = None) -> str:
 
 async def litellm_get_search_query(
     question: str,
+    llm: LLMModel | str,
     count: int,
     template: str | None = None,
-    llm: LLMModel | str = "gpt-4o-mini",
     temperature: float = 1.0,
 ) -> list[str]:
     search_prompt = ""
diff --git a/src/paperqa/agents/models.py b/src/paperqa/agents/models.py
index 7464f355a..36945f927 100644
--- a/src/paperqa/agents/models.py
+++ b/src/paperqa/agents/models.py
@@ -66,7 +66,7 @@ def strip_answer(
         v.filter_content_for_user()
         return v
 
-    async def get_summary(self, llm_model: LLMModel | str = "gpt-4o") -> str:
+    async def get_summary(self, llm_model: LLMModel | str) -> str:
         sys_prompt = (
             "Revise the answer to a question to be a concise SMS message. "
             "Use abbreviations or emojis if necessary."
diff --git a/src/paperqa/settings.py b/src/paperqa/settings.py
index 4d0fdd4dc..b7901c5c0 100644
--- a/src/paperqa/settings.py
+++ b/src/paperqa/settings.py
@@ -11,12 +11,7 @@
 
 import anyio
 from aviary.core import Tool, ToolSelector
-from lmi import (
-    CommonLLMNames,
-    EmbeddingModel,
-    LiteLLMModel,
-    embedding_model_factory,
-)
+from lmi import CommonLLMNames, EmbeddingModel, LiteLLMModel, embedding_model_factory
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -525,7 +520,7 @@ class AgentSettings(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
     agent_llm: str = Field(
-        default=CommonLLMNames.GPT_4O.value,
+        default=CommonLLMNames.GPT_5.value,
         description="LLM inside the agent making tool selections.",
     )
 
@@ -693,7 +688,7 @@ class Settings(BaseSettings):
     model_config = SettingsConfigDict(extra="ignore")
 
     llm: str = Field(
-        default=CommonLLMNames.GPT_4O.value,
+        default=CommonLLMNames.GPT_5.value,
         description=(
             "LLM for general use including metadata inference (see Docs.aadd)"
             " and answer generation (see Docs.aquery and gen_answer tool)."
@@ -717,7 +712,7 @@ class Settings(BaseSettings):
         ),
     )
     summary_llm: str = Field(
-        default=CommonLLMNames.GPT_4O.value,
+        default=CommonLLMNames.GPT_5.value,
         description=(
             "LLM for creating contextual summaries"
             " (see Docs.aget_evidence and gather_evidence tool)."
@@ -741,7 +736,10 @@ class Settings(BaseSettings):
         default=None,
         description="Optional configuration for the embedding model.",
     )
-    temperature: float = Field(default=0.0, description="Temperature for LLMs.")
+    temperature: float = Field(
+        default=1.0,
+        description="Temperature for LLMs, default is 1 for compatibility with OpenAI's GPT-5.",
+    )
     batch_size: int = Field(default=1, description="Batch size for calling LLMs.")
     texts_index_mmr_lambda: float = Field(
         default=1.0, description="Lambda for MMR in text index."
diff --git a/tests/cassettes/test_docs_lifecycle.yaml b/tests/cassettes/test_docs_lifecycle.yaml
index 962c75284..b144437fb 100644
--- a/tests/cassettes/test_docs_lifecycle.yaml
+++ b/tests/cassettes/test_docs_lifecycle.yaml
@@ -1,69 +1,4 @@
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@@ -94,7 +29,7 @@ interactions:
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+          "144557636", "name": "L. Rankin"}], "matchScore": 106.48929}]}
 
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       headers:
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-        \\\"Department of Canadian Heritage\\\"). [\\\"Ceremonial and Canadian Symbols
-        Promotion > The National Flag of Canada\\\"](https://web.archive.org/web/20100423114158/http://www.canadianheritage.gc.ca/progs/cpsc-ccsp/sc-cs/df1_e.cfm).
-        Queen's Printer for Canada. Archived from [the original](http://www.canadianheritage.gc.ca/progs/cpsc-ccsp/sc-cs/df1_e.cfm)
-        on April 23, 2010. Retrieved March 21, 2010.\\n  2. ^ _**a**_ _**b**_ Government
-        of Canada, Public Services and Procurement Canada (July 31, 2015). [\\\"Infographic:
-        National Flag of Canada Day \u2013 February 15 \u2013 Canada's Parliamentary
-        Precinct \u2013 PWGSC\\\"](https://www.tpsgc-pwgsc.gc.ca/citeparlementaire-parliamentaryprecinct/decouvrez-discover/jour-drap-flag-day-eng.html).
-        _www.tpsgc-pwgsc.gc.ca_. Retrieved February 5, 2022.\\n  3. ^ _**a**_ _**b**_
-        [\\\"What is the National Flag Day of Canada?\\\"](http://westernfinancialgroup.ca/What-is-the-National-Flag-of-Canada-Day).
-        _westernfinancialgroup.ca_. Retrieved February 5, 2022.\\n  4. **^** [Department
-        of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department of
-        Canadian Heritage\\\"). [\\\"Ceremonial and Canadian Symbols Promotion > The
-        National Flag of Canada > Birth of the Canadian flag\\\"](http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm).
-        Queen's Printer for Canada. [Archived](https://web.archive.org/web/20100224005050/http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm)
-        from the original on February 24, 2010. Retrieved March 21, 2010.\\n  5. **^**
-        [\\\"Birth of the Canadian flag\\\"](http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm).
-        [Department of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department
-        of Canadian Heritage\\\"). [Archived](https://web.archive.org/web/20081220170253/http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm)
-        from the original on December 20, 2008. Retrieved December 16, 2008.\\n  6.
-        **^** [Conserving the Proclamation of the Canadian Flag](http://www.collectionscanada.gc.ca/publications/archivist-magazine/015002-2021-e.html)
-        [Archived](https://web.archive.org/web/20121021133944/http://www.collectionscanada.gc.ca/publications/archivist-magazine/015002-2021-e.html)
-        October 21, 2012, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback
-        Machine\\\"), Library and Archives of Canada, from John Grace in The Archivist,
-        National Archives, Ottawa, 1990. Retrieved February 15, 2011.\\n  7. **^** [Department
-        of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department of
-        Canadian Heritage\\\"). [\\\"National Flag of Canada Day\\\"](http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm).
-        Queen's Printer for Canada. [Archived](https://web.archive.org/web/20100217042202/http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm)
-        from the original on February 17, 2010. Retrieved March 21, 2010.\\n  8. **^**
-        [Dept. of Canadian Heritage news release](http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
+        chunk 4: \\\"National Flag of Canada Day.\\\" WikiMedia Foundation, 2023, Accessed
+        now\\n\\n------------\\n\\n2010.\\n  8. **^** [Dept. of Canadian Heritage news
+        release](http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
         [Archived](https://web.archive.org/web/20110706182436/http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
         July 6, 2011, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback Machine\\\"),
-        February 15, 2010. Retrieved February 15, 2011.\\n \\n\\n------------\\n\\nQuestion:
-        What is the national flag of Canada?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        February 15, 2010. Retrieved February 15, 2011.\\n  9. **^** [PM pays tribute
+        to outstanding Canadians on Flag Day](http://www.pm.gc.ca/eng/media.asp?category=1&id=3958&featureId=6&pageId=26)
+        [Archived](https://web.archive.org/web/20110706181811/http://www.pm.gc.ca/eng/media.asp?category=1&id=3958&featureId=6&pageId=26)
+        July 6, 2011, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback Machine\\\"),
+        Prime Minister's Office news release. Retrieved February 16, 2011.\\n\\n## External
+        links\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=6\\n\\\"Edit
+        section: External links\\\")]\\n\\n![](//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/30px-\\nCommons-logo.svg.png)\\n\\nWikimedia
+        Commons has media related to [National Flag of Canada\\nDay](https://commons.wikimedia.org/wiki/Category:National_Flag_of_Canada_Day\\n\\\"commons:Category:National
+        Flag of Canada Day\\\").\\n\\n  * [Flag of Canada Song (1965) Freddie Grant](https://www.youtube.com/watch?v=2IkqmkTK46E)\\n
+        \ * [Flag Day](http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm), Dept. of
+        Canadian Heritage \\n  * [The famous Canadian Flag Collection, at Settlers,
+        Rails & Trails Inc, Argyle, Manitoba](http://argylemuseum.wixsite.com/argylemuseum/canadian-flag-collection)\\n\\n![](https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1)\\n\\nRetrieved
+        from\\n\\\"[https://en.wikipedia.org/w/index.php?title=National_Flag_of_Canada_Day&oldid=1231946994](https://en.wikipedia.org/w/index.php?title=National_Flag_of_Canada_Day&oldid=1231946994)\\\"\\n\\n[Categories](/wiki/Help:Category
+        \\\"Help:Category\\\"):\\n\\n  * [1996 establishments in Canada](/wiki/Category:1996_establishments_in_Canada
+        \\\"Category:1996 establishments in Canada\\\")\\n  * [Public holidays in Canada](/wiki/Category:Public_holidays_in_Canada
+        \\\"Category:Public holidays in Canada\\\")\\n  * [February observances](/wiki/Category:February_observances
+        \\\"Category:February observances\\\")\\n  * [Flag days](/wiki/Category:Flag_days
+        \\\"Category:Flag days\\\")\\n  * [Winter events in Canada](/wiki/Category:Winter_events_in_Canada
+        \\\"Category:Winter events in Canada\\\")\\n\\nHidden categories:\\n\\n  * [Webarchive
+        template wayback links](/wiki/Category:Webarchive_template_wayback_links \\\"Category:Webarchive
+        template wayback links\\\")\\n  * [Articles with short description](/wiki/Category:Articles_with_short_description
+        \\\"Category:Articles with short description\\\")\\n  * [Short description matches
+        Wikidata](/wiki/Category:Short_description_matches_Wikidata \\\"Category:Short
+        description matches Wikidata\\\")\\n  * [Use mdy dates from February 2018](/wiki/Category:Use_mdy_dates_from_February_2018
+        \\\"Category:Use mdy dates from February 2018\\\")\\n  * [Infobox holiday with
+        missing field](/wiki/Category:Infobox_holiday_with_missing_field \\\"Category:Infobox
+        holiday with missing field\\\")\\n  * [Infobox holiday fixed day](/wiki/Category:Infobox_holiday_fixed_day
+        \\\"Category:Infobox holiday fixed day\\\")\\n  * [Articles containing French-language
+        text](/wiki/Category:Articles_containing_French-language_text \\\"Category:Articles
+        containing French-language text\\\")\\n  * [Commons category link is on Wikidata](/wiki/Category:Commons_category_link_is_on_Wikidata
+        \\\"Category:Commons category link is on Wikidata\\\")\\n\\n  * This page was
+        last edited on 1 July 2024, at 03:41 (UTC). \\n  * Text is available under the
+        [Creative Commons Attribution-ShareAlike License 4.0](//en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_4.0_International_License)[](//en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_4.0_International_License);
+        additional terms may apply. By using this site, you agree to the [Terms of Use](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Terms_of_Use)
+        and [Privacy Policy](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy).
+        Wikipedia\xAE is a registered trademark of the [Wikimedia Foundation, Inc.](//wikimediafoundation.org/),
+        a non-profit organization. \\n\\n  * [Privacy policy](https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy)\\n
+        \ * [About Wikipedia](/wiki/Wikipedia:About)\\n  * [Disclaimers](/wiki/Wikipedia:General_disclaimer)\\n
+        \ * [Contact Wikipedia](//en.wikipedia.org/wiki/Wikipedia:Contact_us)\\n  *
+        [Code of Conduct](https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Universal_Code_of_Conduct)\\n
+        \ * [Developers](https://developer.wikimedia.org)\\n  * [Statistics](https://stats.wikimedia.org/#/en.wikipedia.org)\\n
+        \ * [Cookie statement](https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Cookie_statement)\\n
+        \ * [Mobile view](//en.m.wikipedia.org/w/index.php?title=National_Flag_of_Canada_Day&mobileaction=toggle_view_mobile)\\n\\n
+        \ * [![Wikimedia Foundation](/static/images/footer/wikimedia-button.svg)](https://wikimediafoundation.org/)\\n
+        \ * [![Powered by MediaWiki](/w/resources/assets/poweredby_mediawiki.svg)](https://www.mediawiki.org/)\\n\\n
+        \ * \\n\\n------------\\n\\nQuestion: What is the national flag of Canada?\\n\\n\"}],\"model\":\"gpt-5-2025-08-07\",\"n\":1,\"temperature\":1.0}"
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-        chunk 4: \\\"National Flag of Canada Day.\\\" WikiMedia Foundation, 2023, Accessed
-        now\\n\\n------------\\n\\n2010.\\n  8. **^** [Dept. of Canadian Heritage news
-        release](http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
+        chunk 3: \\\"National Flag of Canada Day.\\\" WikiMedia Foundation, 2023, Accessed
+        now\\n\\n------------\\n\\n an [Order in\\nCouncil](/wiki/Order_in_Council \\\"Order
+        in Council\\\") from [Governor\\nGeneral](/wiki/Governor_General_of_Canada \\\"Governor
+        General of Canada\\\") [Rom\xE9o\\nLeBlanc](/wiki/Rom%C3%A9o_LeBlanc \\\"Rom\xE9o
+        LeBlanc\\\"), on the initiative of Prime\\nMinister [Jean Chr\xE9tien](/wiki/Jean_Chr%C3%A9tien
+        \\\"Jean Chr\xE9tien\\\").[7] At the\\nfirst Flag Day ceremony in [Hull, Quebec](/wiki/Hull,_Quebec
+        \\\"Hull, Quebec\\\"),\\nChr\xE9tien was confronted by demonstrators against
+        proposed cuts to the\\n[unemployment insurance](/wiki/Unemployment_insurance
+        \\\"Unemployment\\ninsurance\\\") system, and while walking through the crowd
+        he was [grabbed by the\\nneck and pushed aside](/wiki/Shawinigan_Handshake \\\"Shawinigan
+        Handshake\\\") a\\nprotester who had approached him.\\n\\nIn 2010, on the flag's
+        45th anniversary, federal ceremonies were held to mark\\nFlag Day at [Ottawa](/wiki/Ottawa
+        \\\"Ottawa\\\"), [Winnipeg](/wiki/Winnipeg\\n\\\"Winnipeg\\\"), [St. John's](/wiki/St._John%27s,_Newfoundland_and_Labrador
+        \\\"St.\\nJohn's, Newfoundland and Labrador\\\"), and at\\n[Whistler](/wiki/Whistler,_British_Columbia
+        \\\"Whistler, British Columbia\\\") and\\n[Vancouver](/wiki/Vancouver \\\"Vancouver\\\")
+        in conjunction with the [2010 Winter\\nOlympics](/wiki/2010_Winter_Olympics
+        \\\"2010 Winter Olympics\\\") in Vancouver.[8]\\nIn 2011, Prime Minister [Stephen
+        Harper](/wiki/Stephen_Harper \\\"Stephen\\nHarper\\\") observed Flag Day by
+        presenting two citizens, whose work honoured the\\n[military](/wiki/Canadian_Armed_Forces
+        \\\"Canadian Armed Forces\\\"), with Canadian\\nflags that had flown over the
+        [Peace Tower](/wiki/Peace_Tower \\\"Peace Tower\\\").\\nIt was announced as
+        inaugurating an annual recognition of patriotism.[9]\\n\\n## See also\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=4\\n\\\"Edit
+        section: See also\\\")]\\n\\n  * ![flag](//upload.wikimedia.org/wikipedia/commons/thumb/f/fc/Maple_Leaf_%28from_roundel%29.svg/25px-Maple_Leaf_%28from_roundel%29.svg.png)[Canada
+        portal](/wiki/Portal:Canada \\\"Portal:Canada\\\")\\n\\n  * [Flag Day](/wiki/Flag_Day
+        \\\"Flag Day\\\")\\n  * [List of Canadian flags](/wiki/List_of_Canadian_flags
+        \\\"List of Canadian flags\\\")\\n  * [National flag](/wiki/National_flag \\\"National
+        flag\\\")\\n\\n## Footnotes\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=5\\n\\\"Edit
+        section: Footnotes\\\")]\\n\\n  1. **^** [Department of Canadian Heritage](/wiki/Department_of_Canadian_Heritage
+        \\\"Department of Canadian Heritage\\\"). [\\\"Ceremonial and Canadian Symbols
+        Promotion > The National Flag of Canada\\\"](https://web.archive.org/web/20100423114158/http://www.canadianheritage.gc.ca/progs/cpsc-ccsp/sc-cs/df1_e.cfm).
+        Queen's Printer for Canada. Archived from [the original](http://www.canadianheritage.gc.ca/progs/cpsc-ccsp/sc-cs/df1_e.cfm)
+        on April 23, 2010. Retrieved March 21, 2010.\\n  2. ^ _**a**_ _**b**_ Government
+        of Canada, Public Services and Procurement Canada (July 31, 2015). [\\\"Infographic:
+        National Flag of Canada Day \u2013 February 15 \u2013 Canada's Parliamentary
+        Precinct \u2013 PWGSC\\\"](https://www.tpsgc-pwgsc.gc.ca/citeparlementaire-parliamentaryprecinct/decouvrez-discover/jour-drap-flag-day-eng.html).
+        _www.tpsgc-pwgsc.gc.ca_. Retrieved February 5, 2022.\\n  3. ^ _**a**_ _**b**_
+        [\\\"What is the National Flag Day of Canada?\\\"](http://westernfinancialgroup.ca/What-is-the-National-Flag-of-Canada-Day).
+        _westernfinancialgroup.ca_. Retrieved February 5, 2022.\\n  4. **^** [Department
+        of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department of
+        Canadian Heritage\\\"). [\\\"Ceremonial and Canadian Symbols Promotion > The
+        National Flag of Canada > Birth of the Canadian flag\\\"](http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm).
+        Queen's Printer for Canada. [Archived](https://web.archive.org/web/20100224005050/http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm)
+        from the original on February 24, 2010. Retrieved March 21, 2010.\\n  5. **^**
+        [\\\"Birth of the Canadian flag\\\"](http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm).
+        [Department of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department
+        of Canadian Heritage\\\"). [Archived](https://web.archive.org/web/20081220170253/http://www.pch.gc.ca/pgm/ceem-cced/symbl/df3-eng.cfm)
+        from the original on December 20, 2008. Retrieved December 16, 2008.\\n  6.
+        **^** [Conserving the Proclamation of the Canadian Flag](http://www.collectionscanada.gc.ca/publications/archivist-magazine/015002-2021-e.html)
+        [Archived](https://web.archive.org/web/20121021133944/http://www.collectionscanada.gc.ca/publications/archivist-magazine/015002-2021-e.html)
+        October 21, 2012, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback
+        Machine\\\"), Library and Archives of Canada, from John Grace in The Archivist,
+        National Archives, Ottawa, 1990. Retrieved February 15, 2011.\\n  7. **^** [Department
+        of Canadian Heritage](/wiki/Department_of_Canadian_Heritage \\\"Department of
+        Canadian Heritage\\\"). [\\\"National Flag of Canada Day\\\"](http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm).
+        Queen's Printer for Canada. [Archived](https://web.archive.org/web/20100217042202/http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm)
+        from the original on February 17, 2010. Retrieved March 21, 2010.\\n  8. **^**
+        [Dept. of Canadian Heritage news release](http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
         [Archived](https://web.archive.org/web/20110706182436/http://www.pch.gc.ca/pc-ch/infoCntr/cdm-mc/index-eng.cfm?action=doc&DocIDCd=CJM092444)
         July 6, 2011, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback Machine\\\"),
-        February 15, 2010. Retrieved February 15, 2011.\\n  9. **^** [PM pays tribute
-        to outstanding Canadians on Flag Day](http://www.pm.gc.ca/eng/media.asp?category=1&id=3958&featureId=6&pageId=26)
-        [Archived](https://web.archive.org/web/20110706181811/http://www.pm.gc.ca/eng/media.asp?category=1&id=3958&featureId=6&pageId=26)
-        July 6, 2011, at the [Wayback Machine](/wiki/Wayback_Machine \\\"Wayback Machine\\\"),
-        Prime Minister's Office news release. Retrieved February 16, 2011.\\n\\n## External
-        links\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=6\\n\\\"Edit
-        section: External links\\\")]\\n\\n![](//upload.wikimedia.org/wikipedia/en/thumb/4/4a/Commons-logo.svg/30px-\\nCommons-logo.svg.png)\\n\\nWikimedia
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-        Flag of Canada Day\\\").\\n\\n  * [Flag of Canada Song (1965) Freddie Grant](https://www.youtube.com/watch?v=2IkqmkTK46E)\\n
-        \ * [Flag Day](http://www.pch.gc.ca/special/jdn-nfd/index-eng.cfm), Dept. of
-        Canadian Heritage \\n  * [The famous Canadian Flag Collection, at Settlers,
-        Rails & Trails Inc, Argyle, Manitoba](http://argylemuseum.wixsite.com/argylemuseum/canadian-flag-collection)\\n\\n![](https://login.wikimedia.org/wiki/Special:CentralAutoLogin/start?type=1x1)\\n\\nRetrieved
-        from\\n\\\"[https://en.wikipedia.org/w/index.php?title=National_Flag_of_Canada_Day&oldid=1231946994](https://en.wikipedia.org/w/index.php?title=National_Flag_of_Canada_Day&oldid=1231946994)\\\"\\n\\n[Categories](/wiki/Help:Category
-        \\\"Help:Category\\\"):\\n\\n  * [1996 establishments in Canada](/wiki/Category:1996_establishments_in_Canada
-        \\\"Category:1996 establishments in Canada\\\")\\n  * [Public holidays in Canada](/wiki/Category:Public_holidays_in_Canada
-        \\\"Category:Public holidays in Canada\\\")\\n  * [February observances](/wiki/Category:February_observances
-        \\\"Category:February observances\\\")\\n  * [Flag days](/wiki/Category:Flag_days
-        \\\"Category:Flag days\\\")\\n  * [Winter events in Canada](/wiki/Category:Winter_events_in_Canada
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+        \ * [What links here](/wiki/Special:WhatLinksHere/National_Flag_of_Canada_Day
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+        \\\"Structured data on this page hosted by Wikidata \\\\[g\\\\]\\\")\\n\\nPrint/export\\n\\n
+        \ * [Download as PDF](/w/index.php?title=Special:DownloadAsPdf&page=National_Flag_of_Canada_Day&action=show-download-screen
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+        \\\"Printable version of this page \\\\[p\\\\]\\\")\\n\\nIn other projects\\n\\n
+        \ * [Wikimedia Commons](https://commons.wikimedia.org/wiki/Category:National_Flag_of_Canada_Day)\\n\\nAppearance\\n\\nmove
+        to sidebar  hide\\n\\nFrom Wikipedia, the free encyclopedia\\n\\nCanadian holiday\\n\\nNational
+        Flag of Canada Day  \\n---  \\n[![](//upload.wikimedia.org/wikipedia/commons/thumb/6/68/Canada_flag_halifax_9_-04.JPG/250px-\\nCanada_flag_halifax_9_-04.JPG)](/wiki/File:Canada_flag_halifax_9_-04.JPG)
+        The\\nnational flag of Canada  \\nObserved by  |  [Canada](/wiki/Canada \\\"Canada\\\")
+        \ \\nDate |  [February 15](/wiki/February_15 \\\"February 15\\\")  \\nNext time
+        \ |  February 15, 2025 (2025-02-15)  \\nFrequency | Annual  \\n  \\n**National
+        Flag of Canada Day** ([French](/wiki/French_language \\\"French\\nlanguage\\\"):
+        _Jour du drapeau national du Canada_), commonly shortened to\\n**Flag Day**
+        , is observed annually on February 15 to commemorate the\\ninauguration of the
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+        \\\"Public holidays in Canada\\\"),\\nalthough there has been discussion about
+        creating one.\\n\\n## History\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=1\\n\\\"Edit
+        section: History\\\")]\\n\\n### Background\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=2\\n\\\"Edit
+        section: Background\\\")]\\n\\nAmid [much controversy](/wiki/Great_Canadian_flag_debate
+        \\\"Great Canadian flag\\ndebate\\\"), the [Parliament of Canada](/wiki/Parliament_of_Canada
+        \\\"Parliament of\\nCanada\\\") in 1964 voted to adopt a new design for the
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+        by historian [George Stanley](/wiki/George_Stanley \\\"George\\nStanley\\\")\u2014won
+        with a unanimous vote.[3] Under the leadership of [Prime\\nMinister](/wiki/Prime_Minister_of_Canada
+        \\\"Prime Minister of Canada\\\") [Lester\\nPearson](/wiki/Lester_B._Pearson
+        \\\"Lester B. Pearson\\\"), resolutions\\nrecommending the new design were passed
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+        of Canada\\\") on\\nDecember 15, 1964, and by the [Senate](/wiki/Senate_of_Canada
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+        section: Flag Day\\\")]\\n\\nNational Flag of Canada Day was instituted in 1996
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-        of Canada Day\\n\\n7 languages\\n\\n  * [\u0627\u0644\u0639\u0631\u0628\u064A\u0629](https://ar.wikipedia.org/wiki/%D9%8A%D9%88%D9%85_%D8%B9%D9%84%D9%85_%D9%83%D9%86%D8%AF%D8%A7_%D8%A7%D9%84%D9%88%D8%B7%D9%86%D9%8A
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+        External links\\n\\n------------\\n\\nQuestion: What is the national flag of
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+        \\\"How to contact Wikipedia\\\")\\n  * [Donate](https://donate.wikimedia.org/wiki/Special:FundraiserRedirector?utm_source=donate&utm_medium=sidebar&utm_campaign=C13_en.wikipedia.org&uselang=en
+        \\\"Support us by donating to the Wikimedia Foundation\\\")\\n\\nContribute\\n\\n
+        \ * [Help](/wiki/Help:Contents \\\"Guidance on how to use and edit Wikipedia\\\")\\n
+        \ * [Learn to edit](/wiki/Help:Introduction \\\"Learn how to edit Wikipedia\\\")\\n
+        \ * [Community portal](/wiki/Wikipedia:Community_portal \\\"The hub for editors\\\")\\n
+        \ * [Recent changes](/wiki/Special:RecentChanges \\\"A list of recent changes
+        to Wikipedia \\\\[r\\\\]\\\")\\n  * [Upload file](/wiki/Wikipedia:File_upload_wizard
+        \\\"Add images or other media for use on Wikipedia\\\")\\n\\n[ ![](/static/images/icons/wikipedia.png)\\n![Wikipedia](/static/images/mobile/copyright/wikipedia-wordmark-en.svg)
+        ![The\\nFree Encyclopedia](/static/images/mobile/copyright/wikipedia-tagline-en.svg)\\n](/wiki/Main_Page)\\n\\n[
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+        \ * [Create account](/w/index.php?title=Special:CreateAccount&returnto=National+Flag+of+Canada+Day
+        \\\"You are encouraged to create an account and log in; however, it is not mandatory\\\")\\n
+        \ * [Log in](/w/index.php?title=Special:UserLogin&returnto=National+Flag+of+Canada+Day
+        \\\"You're encouraged to log in; however, it's not mandatory. \\\\[o\\\\]\\\")\\n\\nPersonal
+        tools\\n\\n  * [ Create account](/w/index.php?title=Special:CreateAccount&returnto=National+Flag+of+Canada+Day
+        \\\"You are encouraged to create an account and log in; however, it is not mandatory\\\")\\n
+        \ * [ Log in](/w/index.php?title=Special:UserLogin&returnto=National+Flag+of+Canada+Day
+        \\\"You're encouraged to log in; however, it's not mandatory. \\\\[o\\\\]\\\")\\n\\nPages
+        for logged out editors [learn more](/wiki/Help:Introduction)\\n\\n  * [Contributions](/wiki/Special:MyContributions
+        \\\"A list of edits made from this IP address \\\\[y\\\\]\\\")\\n  * [Talk](/wiki/Special:MyTalk
+        \\\"Discussion about edits from this IP address \\\\[n\\\\]\\\")\\n\\n## Contents\\n\\nmove
+        to sidebar  hide\\n\\n  * (Top)\\n  * 1 History Toggle History subsection\\n
+        \   * 1.1 Background\\n    * 1.2 Flag Day\\n  * 2 See also\\n  * 3 Footnotes\\n
+        \ * 4 External links\\n\\nToggle the table of contents\\n\\n#  National Flag
+        of Canada Day\\n\\n7 languages\\n\\n  * [\u0627\u0644\u0639\u0631\u0628\u064A\u0629](https://ar.wikipedia.org/wiki/%D9%8A%D9%88%D9%85_%D8%B9%D9%84%D9%85_%D9%83%D9%86%D8%AF%D8%A7_%D8%A7%D9%84%D9%88%D8%B7%D9%86%D9%8A
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+        \u2013 Arabic\\\")\\n  * [Espa\xF1ol](https://es.wikipedia.org/wiki/D%C3%ADa_de_la_Bandera_Nacional_de_Canad%C3%A1
+        \\\"D\xEDa de la Bandera Nacional de Canad\xE1 \u2013 Spanish\\\")\\n  * [Fran\xE7ais](https://fr.wikipedia.org/wiki/Jour_du_drapeau_national_du_Canada
+        \\\"Jour du drapeau national du Canada \u2013 French\\\")\\n  * [\u0540\u0561\u0575\u0565\u0580\u0565\u0576](https://hy.wikipedia.org/wiki/%D4%BF%D5%A1%D5%B6%D5%A1%D5%A4%D5%A1%D5%B5%D5%AB_%D5%A1%D5%A6%D5%A3%D5%A1%D5%B5%D5%AB%D5%B6_%D5%A4%D6%80%D5%B8%D5%B7%D5%AB_%D6%85%D6%80
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+        \u05E9\u05DC \u05E7\u05E0\u05D3\u05D4 \u2013 Hebrew\\\")\\n  * [Bahasa Melayu](https://ms.wikipedia.org/wiki/Hari_Bendera_Kebangsaan_Kanada
+        \\\"Hari Bendera Kebangsaan Kanada \u2013 Malay\\\")\\n  * [Polski](https://pl.wikipedia.org/wiki/Narodowy_dzie%C5%84_flagi_Kanady
+        \\\"Narodowy dzie\u0144 flagi Kanady \u2013 Polish\\\")\\n\\n[Edit\\nlinks](https://www.wikidata.org/wiki/Special:EntityPage/Q6972703#sitelinks-\\nwikipedia
+        \\\"Edit interlanguage links\\\")\\n\\n  * [Article](/wiki/National_Flag_of_Canada_Day
+        \\\"View the content page \\\\[c\\\\]\\\")\\n  * [Talk](/wiki/Talk:National_Flag_of_Canada_Day
+        \\\"Discuss improvements to the content page \\\\[t\\\\]\\\")\\n\\nEnglish\\n\\n
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-        \ * [What links here](/wiki/Special:WhatLinksHere/National_Flag_of_Canada_Day
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-        \ * [Related changes](/wiki/Special:RecentChangesLinked/National_Flag_of_Canada_Day
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-        \\\"Printable version of this page \\\\[p\\\\]\\\")\\n\\nIn other projects\\n\\n
-        \ * [Wikimedia Commons](https://commons.wikimedia.org/wiki/Category:National_Flag_of_Canada_Day)\\n\\nAppearance\\n\\nmove
-        to sidebar  hide\\n\\nFrom Wikipedia, the free encyclopedia\\n\\nCanadian holiday\\n\\nNational
-        Flag of Canada Day  \\n---  \\n[![](//upload.wikimedia.org/wikipedia/commons/thumb/6/68/Canada_flag_halifax_9_-04.JPG/250px-\\nCanada_flag_halifax_9_-04.JPG)](/wiki/File:Canada_flag_halifax_9_-04.JPG)
-        The\\nnational flag of Canada  \\nObserved by  |  [Canada](/wiki/Canada \\\"Canada\\\")
-        \ \\nDate |  [February 15](/wiki/February_15 \\\"February 15\\\")  \\nNext time
-        \ |  February 15, 2025 (2025-02-15)  \\nFrequency | Annual  \\n  \\n**National
-        Flag of Canada Day** ([French](/wiki/French_language \\\"French\\nlanguage\\\"):
-        _Jour du drapeau national du Canada_), commonly shortened to\\n**Flag Day**
-        , is observed annually on February 15 to commemorate the\\ninauguration of the
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-        and educational programs in schools. It is not a [public\\nholiday](/wiki/Public_holidays_in_Canada
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-        creating one.\\n\\n## History\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=1\\n\\\"Edit
-        section: History\\\")]\\n\\n### Background\\n\\n[[edit](/w/index.php?title=National_Flag_of_Canada_Day&action=edit&section=2\\n\\\"Edit
-        section: Background\\\")]\\n\\nAmid [much controversy](/wiki/Great_Canadian_flag_debate
-        \\\"Great Canadian flag\\ndebate\\\"), the [Parliament of Canada](/wiki/Parliament_of_Canada
-        \\\"Parliament of\\nCanada\\\") in 1964 voted to adopt a new design for the
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-        Red Ensign\\\"), which had been, with various successive alterations,\\nin conventional
-        use as the national flag of [Canada](/wiki/Canada \\\"Canada\\\")\\nsince 1868.
-        Nearly 4,000 designs were submitted by Canadians.[2] On October\\n22, 1964,
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-        by historian [George Stanley](/wiki/George_Stanley \\\"George\\nStanley\\\")\u2014won
-        with a unanimous vote.[3] Under the leadership of [Prime\\nMinister](/wiki/Prime_Minister_of_Canada
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-        D. White. A perspective on explanations of molecular prediction models. Journal
+        pages 3-5: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
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+        leading peer-reviewed journal.\\n\\n------------\\n\\n a passive characteristic
+        of a model, whereas explainability\\n\\nis an active characteristic which is
+        used to clarify the internal decision-making process.\\n\\nNamely, an explanation
+        is extra information that gives the context and a cause for one or\\n\\nmore
+        predictions.29 We adopt the same nomenclature in this perspective.\\n\\n   Accuracy
+        and interpretability are two attractive characteristics of DL models. However,\\n\\nDL
+        models are often highly accurate and less interpretable.28,30 XAI provides a
+        way to avoid\\n\\nthat trade-off in chemical property prediction. XAI can be
+        viewed as a two-step process.\\n\\nFirst, we develop an accurate but uninterpretable
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+        should give insight into the underlying mechanism.\\n\\n   In the remainder
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+        various systems these methods yield explanations that are consistent with known
+        and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n                                       3Theory\\n\\n\\nIn
+        this work, we aim to assemble a common taxonomy for the landscape of XAI while\\n\\nproviding
+        our perspectives. We utilized the vocabulary proposed by Das and Rad 32 to classify\\n\\nXAI.
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+        found in the literature focus on interpreting\\n\\nmodels through 1) training
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+        In instances where such mechanistic knowledge is sparse, expert bi-\\n\\nases
+        and subjectivity can be used to measure the correctness.40 Other similar metrics
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+        explanations. The aim of this study is to bridge the gap between\\n\\nanalysis
+        of XAI methods. Based on the above discussion, we identify that an agreed upon\\n\\n\\n
+        \                                      4evaluation metric is necessary in XAI.
+        We suggest the following attributes can be used to\\n\\nevaluate explanations.
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-        leading peer-reviewed journal.\\n\\n------------\\n\\n2021, 25, 1315\u20131360.\\n\\n\\n
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-        \    to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for
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-        K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n     Explainable Artificial
-        Intelligence (XAI) techniques for energy and power systems:\\n\\n     Review,
-        challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh,
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-        Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D
-        Explaining the\\n\\n     predictions of any classifier. Proceedings of the 22nd
-        ACM SIGKDD international\\n\\n\\n                                      27     conference
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+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
+        leading peer-reviewed journal.\\n\\n------------\\n\\nnal molecule.  The counterfactual
+        indicates\\nstructural changes to ethyl benzoate that would result in the model
+        predicting the molecule\\nto not contain the \u2018fruity\u2019 scent. The Tanimoto96
+        similarity between the counterfactual and\\n2,4 decadienal is also provided.
+        Republished with permission from authors.31\\n\\n\\n   The molecule 2,4-decadienal,
+        which is known to have a \u2018fatty\u2019 scent, is analyzed in Fig-\\n\\nure
+        5.142,143 The resulting counterfactual, which has a shorter carbon chain and
+        no carbonyl\\n\\ngroups, highlights the influence of these structural features
+        on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. To generalize to other
+        molecules, Seshadri et al. 31 applied the descriptor attribution\\n\\nmethod
+        to obtain global explanations for the scents. The global explanation for the
+        \u2018fatty\u2019\\n\\nscent was generated by gathering chemical spaces around
+        many \u2018fatty\u2019 scented molecules.\\n\\nThe resulting natural language
+        explanation is: \u201CThe molecular property \u201Cfatty scent\u201D can\\n\\nbe
+        explained by the presence of a heptanyl fragment, two CH2 groups separated by
+        four\\n\\n\\n                                       20bonds, and a C=O double
+        bond, as well as the lack of more than one or two O atoms.\u201D31\\n\\nThe
+        importance of a heptanyl fragment aligns with that reported in the literature,
+        as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore,
+        the importance of a C=O dou-\\n\\nble bond is supported by the findings reported
+        by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D,
+        they found that \u2018fatty\u2019 molecules also had \u201Caldehyde or acid\\n\\nfunctions\u201D.145
+        For the \u2018pineapple\u2019 scent, the following natural language explanation
+        was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D
+        can be explained by the presence of ester,\\n\\nethyl/ether O group, alkene/ether
+        O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31
+        Esters, such as ethyl 2-methylbutyrate, are present in many pineap-\\n\\nple
+        volatile compounds.146,147 The combination of a C=O double bond with an ether
+        could\\n\\nalso correspond to an ester group. Additionally, aldehydes and ketones,
+        which contain C=O\\n\\ndouble bonds, are also common in pineapple volatile compounds.146,148\\n\\n\\nDiscussion\\n\\n\\nWe
+        have shown two post-hoc XAI applications based on molecular counterfactual expla-\\n\\nnations9
+        and descriptor explanations.10 These methods can be used to explain black-box\\n\\nmodels
+        whose input is a molecule. These two methods can be applied for both classification\\n\\nand
+        regression tasks. Note that the \u201Ccorrectness\u201D of the explanations
+        strongly depends on\\n\\nthe accuracy of the black-box model.\\n\\n  A molecular
+        counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith
+        contrasting chemical properties.  In the above examples, we used Tanimoto similar-\\n\\nity96
+        of ECFP4 fingreprints97 as distance, although this should be explored in the
+        future.\\n\\nCounterfactual explanations are useful because they are represented
+        as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable.
+        A few other popular examples of\\n\\ncounterfactual on graph methods are GNNExplainer,
+        MEG and CF-GNNExplainer.69,104,105\\n\\n   The descriptor explanation method
+        developed by Gandhi and White 10 fits a self-explaining\\n\\n\\n\\n                                       21surrogate
+        model to explain the black-box model. This is similar to the GraphLIME87 method,\\n\\nalthough
+        we have the flexibility to use explanation features other than subgraphs. Futher-\\n\\nmore,
+        we show that natural language combined with chemical descriptor attributions
+        can\\n\\ncreate explanations useful for chemists, thus enhancing the accessibility
+        of DL in chemistry.\\n\\nLastly, we examined if XAI can be used beyond interpretation.
+        Work by Seshadri et al. 31 use\\n\\nMMACE and surrogate model explanations to
+        analyze the structure-property relationships\\n\\nof scent. They recovered known
+        structure-property relationships for molecular scent purely\\n\\nfrom explanations,
+        demonstrating the usefulness of a two step process: fit an accurate model\\n\\nand
+        then explain it.\\n\\n   Choosing among the plethora of XAI methods described
+        here is still an open question.\\n\\nIt remains to be seen if there will ever
+        be a consensus benchmark, since this field sits on\\n\\nthe intersection of
+        human-machine interaction, machine learning, and philosophy (i.e., what\\n\\nconstitutes
+        an explanation?). Our current advice is to consider first the audience \u2013
+        domain\\n\\nexperts or ML experts or non-experts \u2013 and what the explanations
+        should accomplish. Are\\n\\nthey meant to inform data selection or model building,
+        how a prediction is used, or how the\\n\\nfeatures can be changed to affect
+        the outcome. The second consideration is what access you\\n\\nhave to the underlying
+        model. The ability to have model derivatives or propagate gradients\\n\\nto
+        the input to models informs the XAI method.\\n\\n\\nConclusion and outlook\\n\\n\\nWe
+        should seek to explain molecular property prediction models because users are
+        more\\n\\nlikely to trust explained predictions, and explanations can help assess
+        if the model is learning\\n\\nt\\n\\n------------\\n\\nQuestion: What is XAI?\\n\\n\"}],\"model\":\"gpt-5-2025-08-07\",\"n\":1,\"temperature\":1.0}"
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\"}},{\"type\":\"text\",\"text\":\"Excerpt
+        from wellawatte2023aperspectiveon pages 14-16: Geemi P. Wellawatte, Heta A.
+        Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of
+        molecular prediction models. Journal of Chemical Theory and Computation, 19:2149-2160,
+        Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
+        This article has 52 citations and is from a domain leading peer-reviewed journal.\\n\\n------------\\n\\nsame
+        optimization problem.100 Grabocka\\n\\net al. 111 have developed a method named
+        Adversarial Training on EXplanations (ATEX)\\n\\nwhich improves model robustness
+        via exposure to adversarial examples.  While there are\\n\\nconceptual disparities,
+        we note that the counterfactual and adversarial explanations are\\n\\nequivalent
+        mathematical objects.\\n\\n   Matched molecular pairs (MMPs) are pairs of molecules
+        that differ structurally at only\\n\\none site by a known transformation.112,113
+        MMPs are widely used in drug discovery and\\n\\nmedicinal chemistry as these
+        facilitate fast and easy understanding of structure-activity re-\\n\\nlationships.114\u2013116
+        Counterfactuals and MMP examples intersect if the structural change is\\n\\nassociated
+        with a significant change in the properties. In the case the associated changes
+        in\\n\\nthe properties are non-significant, the two molecules are known as bioisosteres.117,118
+        The con-\\n\\nnection between MMPs and adversarial training examples has been
+        explored by van Tilborg\\n\\net al. 119. MMPs which belong to the counterfactual
+        category are commonly used in outlier\\n\\nand activity cliff detection.113
+        This approach is analogous to counterfactual explanations,\\n\\nas the common
+        objective is to uncover learned knowledge pertaining to structure-property\\n\\nrelationships.70\\n\\n\\nApplications\\n\\n\\nModel
+        interpretation is certainly not new and a common step in ML in chemistry, but
+        XAI for\\n\\nDL models is becoming more important60,66\u201369,73,88,104,105
+        Here we illustrate some practical\\n\\nexamples drawn from our published work
+        on how model-agnostic XAI can be utilized to\\n\\n\\n\\n                                       14interpret
+        black-box models and connect the explanations to structure-property relationships.\\n\\nThe
+        methods are \u201CMolecular Model Agnostic Counterfactual Explanations\u201D
+        (MMACE)9\\n\\nand \u201CExplaining molecular properties with natural language\u201D.10
+        Then we demonstrate how\\n\\ncounterfactuals and descriptor explanations can
+        propose structure-property relationships in\\n\\nthe domain of molecular scent.31\\n\\n\\nBlood-brain
+        barrier permeation prediction\\n\\n\\nThe passive diffusion of drugs from the
+        blood stream to the brain is a critical aspect in drug\\n\\ndevelopment and
+        discovery.120 Small molecule blood-brain barrier (BBB) permeation is a\\n\\nclassification
+        problem routinely assessed with DL models.121,122 To explain why DL models\\n\\nwork,
+        we trained two models a random forest (RF) model123 and a Gated Recurrent Unit\\n\\nRecurrent
+        Neural Network (GRU-RNN). Then we explained the RF model with generated\\n\\ncounterfactuals
+        explanations using the MMACE9 and the GRU-RNN with descriptor expla-\\n\\nnations.10
+        Both the models were trained on the dataset developed by Martins et al. 124.
+        The\\n\\nRF model was implemented in Scikit-learn125 using Mordred molecular
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+        in Keras.127 See Wellawatte et al. 9\\n\\nand Gandhi and White 10 for more details.\\n\\n
+        \  According to the counterfactuals of the instance molecule in figure 1, we
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+        negative example molecule to permeate\\n\\nthe BBB. Experimental findings by
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+        by hydrophobic interactions and surface area. The carboxylic group is\\n\\na
+        hydrophilic functional group which hinders hydrophobic interactions and addition
+        of atoms\\n\\nenhances the surface area. This proves the advantage of using
+        counterfactual explanations,\\n\\nas they suggest actionable modification to
+        the molecule to make it cross the BBB.\\n\\n   In Figure 2 we show descriptor
+        explanations generated for Alprozolam, a molecule that\\n\\npermeates the BBB,
+        using the method described by Gandhi and White 10. We see that\\n\\npredicted
+        permeability is positively correlated with the aromaticity of the molecule,
+        while\\n\\n\\n                                       15negatively correlated
+        with the number of hydrogen bonds donors and acceptors. A similar\\n\\nstructure-property
+        relationship for BBB permeability is proposed in more mechanistic stud-\\n\\nies.128\u2013130
+        The substructure attributions indicates a reduction in hydrogen bond donors
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+        by chemists.\\n\\nFinally, we can use a natural language model to summarize
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+        ECFP4 fingerprints.131 Red indicates deletions and\\ngreen indicates substitutions
+        and addition of atoms. Republished from Ref.9 with permission\\nfrom the Royal
+        Society of Chemistry.\\n\\n\\n\\nSolubility prediction\\n\\n\\nSmall molecule
+        solubility prediction is a classic cheminformatics regression challenge and
+        is\\n\\nimportant for chemical process design, drug design and crystallization.133\u2013136
+        In our previous\\n\\nworks,9,10 we implemented and trained an RNN model in Keras
+        to predict solubilities (log\\n\\nmolarity) of small molecules.127 The AqS\\n\\n------------\\n\\nQuestion:
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\"}},{\"type\":\"text\",\"text\":\"Excerpt
-        from wellawatte2023aperspectiveon pages 14-16: Geemi P. Wellawatte, Heta A.
-        Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of
-        molecular prediction models. Journal of Chemical Theory and Computation, 19:2149-2160,
-        Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
-        This article has 50 citations and is from a domain leading peer-reviewed journal.\\n\\n------------\\n\\nsame
-        optimization problem.100 Grabocka\\n\\net al. 111 have developed a method named
-        Adversarial Training on EXplanations (ATEX)\\n\\nwhich improves model robustness
-        via exposure to adversarial examples.  While there are\\n\\nconceptual disparities,
-        we note that the counterfactual and adversarial explanations are\\n\\nequivalent
-        mathematical objects.\\n\\n   Matched molecular pairs (MMPs) are pairs of molecules
-        that differ structurally at only\\n\\none site by a known transformation.112,113
-        MMPs are widely used in drug discovery and\\n\\nmedicinal chemistry as these
-        facilitate fast and easy understanding of structure-activity re-\\n\\nlationships.114\u2013116
-        Counterfactuals and MMP examples intersect if the structural change is\\n\\nassociated
-        with a significant change in the properties. In the case the associated changes
-        in\\n\\nthe properties are non-significant, the two molecules are known as bioisosteres.117,118
-        The con-\\n\\nnection between MMPs and adversarial training examples has been
-        explored by van Tilborg\\n\\net al. 119. MMPs which belong to the counterfactual
-        category are commonly used in outlier\\n\\nand activity cliff detection.113
-        This approach is analogous to counterfactual explanations,\\n\\nas the common
-        objective is to uncover learned knowledge pertaining to structure-property\\n\\nrelationships.70\\n\\n\\nApplications\\n\\n\\nModel
-        interpretation is certainly not new and a common step in ML in chemistry, but
-        XAI for\\n\\nDL models is becoming more important60,66\u201369,73,88,104,105
-        Here we illustrate some practical\\n\\nexamples drawn from our published work
-        on how model-agnostic XAI can be utilized to\\n\\n\\n\\n                                       14interpret
-        black-box models and connect the explanations to structure-property relationships.\\n\\nThe
-        methods are \u201CMolecular Model Agnostic Counterfactual Explanations\u201D
-        (MMACE)9\\n\\nand \u201CExplaining molecular properties with natural language\u201D.10
-        Then we demonstrate how\\n\\ncounterfactuals and descriptor explanations can
-        propose structure-property relationships in\\n\\nthe domain of molecular scent.31\\n\\n\\nBlood-brain
-        barrier permeation prediction\\n\\n\\nThe passive diffusion of drugs from the
-        blood stream to the brain is a critical aspect in drug\\n\\ndevelopment and
-        discovery.120 Small molecule blood-brain barrier (BBB) permeation is a\\n\\nclassification
-        problem routinely assessed with DL models.121,122 To explain why DL models\\n\\nwork,
-        we trained two models a random forest (RF) model123 and a Gated Recurrent Unit\\n\\nRecurrent
-        Neural Network (GRU-RNN). Then we explained the RF model with generated\\n\\ncounterfactuals
-        explanations using the MMACE9 and the GRU-RNN with descriptor expla-\\n\\nnations.10
-        Both the models were trained on the dataset developed by Martins et al. 124.
-        The\\n\\nRF model was implemented in Scikit-learn125 using Mordred molecular
-        descriptors126 as the\\n\\ninput features. The GRU-RNN model was implemented
-        in Keras.127 See Wellawatte et al. 9\\n\\nand Gandhi and White 10 for more details.\\n\\n
-        \  According to the counterfactuals of the instance molecule in figure 1, we
-        observe that the\\n\\nmodifications to the carboxylic acid group enable the
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-        of atoms\\n\\nenhances the surface area. This proves the advantage of using
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-        solubility prediction is a classic cheminformatics regression challenge and
-        is\\n\\nimportant for chemical process design, drug design and crystallization.133\u2013136
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+        D. White. A perspective on explanations of molecular prediction models. Journal
+        of Chemical Theory and Computation, 19:2149-2160, Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235,
+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
+        leading peer-reviewed journal.\\n\\n------------\\n\\n2021, 25, 1315\u20131360.\\n\\n\\n
+        (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation
+        of counter-\\n\\n     factual explanations for molecules. Chemical Science 2022,
+        13, 3697\u20133705.\\n\\n\\n(10) Gandhi, H. A.; White, A. D. Explaining structure-activity
+        relationships using locally\\n\\n      faithful surrogate models. chemrxiv 2022,\\n\\n\\n(11)
+        Gormley, A. J.; Webb, M. A. Machine learning in combinatorial polymer chemistry.\\n\\n
+        \    Nature Reviews Materials 2021,\\n\\n\\n(12) Gomes, C. P.; Fink, D.; Dover,
+        R. B. V.; Gregoire, J. M. Computational sustainability\\n\\n     meets materials
+        science. Nature Reviews Materials 2021,\\n\\n\\n(13) On scientific understanding
+        with artificial intelligence. Nature Reviews Physics 2022\\n\\n     4:12 2022,
+        4, 761\u2013769.\\n\\n\\n(14) Arrieta, A. B.; D\xB4\u0131az-Rodr\xB4\u0131guez,
+        N.; Ser, J. D.; Bennetot, A.; Tabik, S.; Barbado, A.;\\n\\n     Garcia, S.;
+        Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F. Explain-\\n\\n
+        \    able Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities
+        and Chal-\\n\\n     lenges toward Responsible AI. Information Fusion 2019, 58,
+        82\u2013115.\\n\\n\\n(15) Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl,
+        R.; Yu, B. Interpretable machine\\n\\n     learning: definitions, methods, and
+        applications. ArXiv 2019, abs/1901.04592.\\n\\n\\n                                      25(16)
+        Boobier, S.; Osbourn, A.; Mitchell, J. B. Can human experts predict solubility
+        better\\n\\n     than computers? Journal of cheminformatics 2017, 9, 1\u201314.\\n\\n\\n(17)
+        Lee, J. D.; See, K. A. Trust in automation: Designing for appropriate reliance.
+        Human\\n\\n     Factors 2004, 46, 50\u201380.\\n\\n\\n(18) Bolukbasi, T.; Chang,
+        K.-W.; Zou, J. Y.; Saligrama, V.; Kalai, A. T. Man is to com-\\n\\n     puter
+        programmer as woman is to homemaker? debiasing word embeddings. Advances\\n\\n
+        \    in neural information processing systems 2016, 29.\\n\\n\\n(19) Buolamwini,
+        J.; Gebru, T. Gender Shades:  Intersectional Accuracy Disparities in\\n\\n    Commercial
+        Gender Classification. Proceedings of the 1st Conference on Fairness,\\n\\n
+        \    Accountability and Transparency. 2018; pp 77\u201391.\\n\\n\\n(20) Lapuschkin,
+        S.; W\xA8aldchen, S.; Binder, A.; Montavon, G.; Samek, W.; M\xA8uller, K.-R.\\n\\n
+        \   Unmasking Clever Hans predictors and assessing what machines really learn.
+        Nature\\n\\n     communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A.
+        J.; Janizek, J. D.; Lee, S.-I. AI for radiographic COVID-19 detection\\n\\n
+        \     selects shortcuts over signal. Nature Machine Intelligence 2021, 3, 610\u2013619.\\n\\n\\n(22)
+        Goodman, B.; Flaxman, S. European Union regulations on algorithmic decision-\\n\\n
+        \   making and a \u201Cright to explanation\u201D. AI Magazine 2017, 38, 50\u201357.\\n\\n\\n(23)
+        ACT, A. I. European Commission. On Artificial Intelligence: A European Approach\\n\\n
+        \    to Excellence and Trust. 2021, COM/2021/206.\\n\\n\\n(24) Blueprint for
+        an AI Bill of Rights, The White House. 2022; https://www.whitehouse.\\n\\n    gov/ostp/ai-bill-of-rights/.\\n\\n\\n(25)
+        Miller, T. Explanation in artificial intelligence: Insights from the social
+        sciences. Ar-\\n\\n       tificial intelligence 2019, 267, 1\u201338.\\n\\n\\n\\n
+        \                                     26(26) Murdoch, W. J.; Singh, C.; Kumbier,
+        K.; Abbasi-Asl, R.; Yu, B. Definitions, meth-\\n\\n     ods, and applications
+        in interpretable machine learning. Proceedings of the National\\n\\n    Academy
+        of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27)
+        Gunning, D.; Aha, D. DARPA\u2019s Explainable Artificial Intelligence (XAI)
+        Program.\\n\\n    AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.;
+        Cotton, C. Explanation and justification in machine learning: A survey.\\n\\n
+        \    IJCAI-17 workshop on explainable AI (XAI). 2017; pp 8\u201313.\\n\\n\\n(29)
+        Palacio, S.; Lucieri, A.; Munir, M.; Ahmed, S.; Hees, J.; Dengel, A. Xai handbook:\\n\\n
+        \   Towards a unified framework for explainable ai. Proceedings of the IEEE/CVF
+        Inter-\\n\\n     national Conference on Computer Vision. 2021; pp 3766\u20133775.\\n\\n\\n(30)
+        Kuhn, D. R.; Kacker, R. N.; Lei, Y.; Simos, D. E. Combinatorial Methods for
+        Ex-\\n\\n     plainable AI. 2020 IEEE International Conference on Software Testing,
+        Verification\\n\\n    and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31)
+        Seshadri, A.; Gandhi, H. A.; Wellawatte, G. P.; White, A. D. Why does that molecule\\n\\n
+        \    smell? ChemRxiv 2022,\\n\\n\\n(32) Das, A.; Rad, P. Opportunities and challenges
+        in explainable artificial intelligence\\n\\n      (xai): A survey. arXiv preprint
+        arXiv:2006.11371 2020,\\n\\n\\n(33) Machlev, R.; Heistrene, L.; Perl, M.; Levy,
+        K. Y.; Belikov, J.; Mannor, S.; Levron, Y.\\n\\n     Explainable Artificial
+        Intelligence (XAI) techniques for energy and power systems:\\n\\n     Review,
+        challenges and opportunities. Energy and AI 2022, 9, 100169.\\n\\n\\n(34) Koh,
+        P. W.; Liang, P. Understanding black-box predictions via influence functions.\\n\\n
+        \    International Conference on Machine Learning. 2017; pp 1885\u20131894.\\n\\n\\n(35)
+        Ribeiro, M. T.; Singh, S.; Guestrin, C. \u201D Why should i trust you?\u201D
+        Explaining the\\n\\n     predictions of any classifier. Proceedings of the 22nd
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+        Explainable artificial intelligence (XAI) is a branch of AI that addresses the
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+        correlations. Related terms: interpretability is the degree of human understandability
+        intrinsic to a model; justifications are quantitative metrics (e.g., test error)
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+        is an active process clarifying the model\u2019s internal decision-making.\nFrom
         Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A
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-        groups in scent prediction, demonstrating how XAI can derive insights directly
-        from data.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
+        leading peer-reviewed journal.\n\npqac-c9bc1a47: XAI stands for Explainable
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+        decisions understandable and trustworthy. The excerpt cites foundational surveys
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+        \u2018right to explanation\u2019 policies (EU regulations, AI Bill of Rights).
+        It highlights risks like bias and shortcut learning (e.g., Buolamwini & Gebru;
+        Lapuschkin; DeGrave) that motivate explanations. Representative techniques include
+        locally faithful surrogate models (e.g., LIME), influence functions, and combinatorial
+        methods. Overall, XAI provides concepts, taxonomies, and tools to explain and
+        justify predictions toward responsible AI.\nFrom Geemi P. Wellawatte, Heta A.
+        Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of
+        molecular prediction models. Journal of Chemical Theory and Computation, 19:2149-2160,
+        Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
+        This article has 52 citations and is from a domain leading peer-reviewed journal.\n\npqac-4d8428ca:
+        Explainable AI (XAI) is the active process of adding explanations to model predictions
+        to clarify a model\u2019s internal decision-making. While interpretability is
+        a passive property of a model, explainability provides extra information that
+        gives context and causal reasons for one or more predictions. In practice, XAI
+        often uses a two-step approach: train an accurate but opaque deep model, then
+        attach post-hoc explanations that ideally reflect true mechanisms. XAI methods
+        can be global or local (what is explained) and intrinsic (white-box, self-explanatory)
+        or extrinsic/post-hoc. Common post-hoc techniques include feature attribution,
+        surrogate models, and counterfactual or contrastive explanations.\nFrom Geemi
+        P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective
+        on explanations of molecular prediction models. Journal of Chemical Theory and
+        Computation, 19:2149-2160, Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235,
+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
+        leading peer-reviewed journal.\n\npqac-8442093c: XAI (explainable AI) refers
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+        changes that alter a model\u2019s output\u2014and descriptor explanations, which
+        fit a self-explaining surrogate to attribute predictions to interpretable chemical
+        features and generate natural-language rationales. These approaches work for
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+        domain experts, and can uncover structure\u2013property relationships. Selecting
+        an XAI method depends on the audience and goal, and on access to model internals
+        (e.g., gradients). The field bridges ML, human\u2013machine interaction, and
+        philosophy.\nFrom Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
         D. White. A perspective on explanations of molecular prediction models. Journal
         of Chemical Theory and Computation, 19:2149-2160, Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235,
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+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
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+        et al.; Zhang et al.; Yalcin et al.; Hoffman et al.; Mohseni et al.), and inherently
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+        Plumb et al.; influence constraints). It highlights applications and tools in
+        chemistry/materials (Oviedo et al.; Humer et al.; Harren et al.).\nFrom Geemi
+        P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective
+        on explanations of molecular prediction models. Journal of Chemical Theory and
+        Computation, 19:2149-2160, Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235,
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diff --git a/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml b/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml
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           - "true"
         x-stainless-read-timeout:
@@ -3153,122 +3153,122 @@ interactions:
     response:
       body:
         string: !!binary |
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+        S.; Gastegger, M.; M\xA8uller, K.-\\n\\n      R.; Tkatchenko, A. Combining Machine
+        Learning and Computational Chemistry for\\n\\n      Predictive Insights Into
+        Chemical Systems. Chemical Reviews 2021, 121, 9816\u20139872,\\n\\n     PMID:
+        34232033.\\n\\n\\n  (3) Goh, G. B.; Hodas, N. O.; Vishnu, A. Deep learning for
+        computational chemistry.\\n\\n      Journal of Computational Chemistry 2017,
+        38, 1291\u20131307.\\n\\n\\n  (4) Deringer, V. L.; Caro, M. A.; Cs\xB4anyi,
+        G. Machine Learning Interatomic Potentials as\\n\\n     Emerging Tools for Materials
+        Science. Advanced Materials 2019, 31, 1902765.\\n\\n\\n  (5) Faber, F. A.; Hutchison,
+        L.; Huang, B.; Gilmer, J.; Schoenholz, S. S.; Dahl, G. E.;\\n\\n      Vinyals,
+        O.; Kearnes, S.; Riley, P. F.; von Lilienfeld, O. A. Prediction Errors of Molec-\\n\\n
+        \     ular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical\\n\\n
+        \    Theory and Computation 2017, 13, 5255\u20135264, PMID: 28926232.\\n\\n\\n\\n
+        \                                      24 (6) Duch, W.; Swaminathan, K.; Meller,
+        J. Artificial Intelligence Approaches for Rational\\n\\n    Drug Design and
+        Discovery. Current Pharmaceutical Design 2007, 13, 1497\u20131508.\\n\\n\\n
+        (7) Dara, S.; Dhamercherla, S.; Jadav, S. S.; Babu, C. M.; Ahsan, M. J.; darasuresh,
+        S. D.;\\n\\n     Dara, S. Machine Learning in Drug Discovery: A Review. Artificial
+        Intelligence Review\\n\\n     123, 55, 1947\u20131999.\\n\\n\\n (8) Gupta, R.;
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+        (9) Wellawatte, G. P.; Seshadri, A.; White, A. D. Model agnostic generation
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         Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A
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-        A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
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+        doi:10.1021/acs.jctc.2c01235. This article has 52 citations and is from a domain
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+        decisions to experts and non-experts, linking explanations to local chemical
+        space and activity cliffs, and the lack of a universal framework to evaluate
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+        Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations of
+        molecular prediction models. Journal of Chemical Theory and Computation, 19:2149-2160,
+        Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
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+        5.142,143 The resulting counterfactual, which has a shorter carbon chain and
+        no carbonyl\\n\\ngroups, highlights the influence of these structural features
+        on the \u2018fatty\u2019 scent of 2,4 deca-\\n\\ndienal. To generalize to other
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+        four\\n\\n\\n                                       20bonds, and a C=O double
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+        as \u2018fatty\u2019\\n\\nmolecules often have a long carbon chain.144 Furthermore,
+        the importance of a C=O dou-\\n\\nble bond is supported by the findings reported
+        by Licon et al. 145, where in addition to a\\n\\n\u201Clarger carbon-chain skeleton\u201D,
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+        For the \u2018pineapple\u2019 scent, the following natural language explanation
+        was ob-\\n\\ntained: \u201CThe molecular property \u201Cpineapple scent\u201D
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-        D. White. A perspective on explanations of molecular prediction models. ChemRxiv,
-        Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02.
-        This article has 1 citations.\\n\\n------------\\n\\nnal molecule.  The counterfactual
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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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+        from wellawatteUnknownyearaperspectiveon pages 16-20: Geemi P. Wellawatte, Heta
+        A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
+        of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02,
+        doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nssion
+        challenge and is\\n\\nimportant for chemical process design, drug design and
+        crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented
+        and trained an RNN model in Keras to predict solubilities (log\\n\\nmolarity)
+        of small molecules.127 The AqSolDB curated database137 was used to train the\\n\\nRNN
+        model.\\n\\n   In this task, counterfactuals are based on equation 6. Figure
+        3 illustrates the generated\\n\\nlocal chemical space and the top four counterfactuals.
+        Based on the counterfactuals, we ob-\\n\\nserve that the modifications to the
+        ester group and other heteroatoms play an important role\\n\\nin solubility.
+        These findings align with known experimental and basic chemical intuition.134\\n\\nFigure
+        4 shows a quantitative measurement of how substructures are contributing to
+        the pre-\\n\\n\\n\\n                                       16Figure 2: Descriptor
+        explanations along with natural language explanation obtained for BBB\\npermeability
+        of Alprozolam molecule. The green and red bars show descriptors that influ-\\nence
+        predictions positively and negatively, respectively. Dotted yellow lines show
+        significance\\nthreshold (\u03B1 = 0.05) for the t-statistic. Molecular descriptors
+        show molecule-level proper-\\nties that are important for the prediction. ECFP
+        and MACCS descriptors indicate which\\nsubstructures influence model predictions.
+        MACCS explanations lead to text explanations\\nas shown. Republished from Ref.10
+        with permission from authors. SMARTS annotations for\\nMACCS descriptors were
+        created using SMARTSviewer (smartsview.zbh.uni-hamburg.de,\\nCopyright: ZBH,
+        Center for Bioinformatics Hamburg) developed by Schomburg et al. 132.\\n\\n\\n\\n\\n\\n
+        \                                      17diction. For example, we see that adding
+        acidic and basic groups as well as hydrogen bond\\n\\nacceptors, increases solubility.
+        Substructure importance from ECFP97 and MACCS138 de-\\n\\nscriptors indicate
+        that adding heteroatoms increases solubility, while adding rings structures\\n\\nmakes
+        the molecule less soluble. Although these are established hypotheses, it is
+        interesting\\n\\nto see they can be derived purely from the data via DL and
+        XAI.\\n\\n\\n\\n\\n\\nFigure 3: Generated chemical space for solubility prediction
+        using the RNN model. The\\nchemical space is a 2D projection of the pairwise
+        Tanimoto similarities of the local coun-\\nterfactuals. Each data point is colored
+        by solubility. Top 4 counterfactuals are shown here.\\nRepublished from Ref.9
+        with permission from the Royal Society of Chemistry.\\n\\n\\n\\nGeneralizing
+        XAI \u2013 interpreting scent-structure relationships\\n\\n\\nIn this example,
+        we show how non-local structure-property relationships can be learned with\\n\\nXAI
+        across multiple molecules. Molecular scent prediction is a multi-label classification
+        task\\n\\nbecause a molecule can be described by more than one scent. For example,
+        the molecule\\n\\njasmone can be described as having \u2018jasmine,\u2019 \u2018woody,\u2019
+        \u2018floral,\u2019 and \u2019herbal\u2019 scents.139 The\\n\\nscent-structure
+        relationship is not very well understood,140 although some relationships are\\n\\nknown.
+        \ For example, molecules with an ester functional group are often associated
+        with\\n\\n\\n                                       18Figure 4: Descriptor explanations
+        for solubility prediction model. The green and red bars\\nshow descriptors that
+        influence predictions positively and negatively, respectively. Dotted\\nyellow
+        lines show significance threshold (\u03B1 = 0.05) for the t-statistic. The MACCS
+        and\\nECFP descriptors indicate which substructures influence model predictions.
+        MACCS sub-\\nstructures may either be present in the molecule as is or may represent
+        a modification. ECFP\\nfingerprints are substructures in the molecule that affect
+        the prediction. MACCS descriptor\\nare used to obtain text explanations as shown.
+        Republished from Ref.10 with permission from\\nauthors. SMARTS annotations for
+        MACCS descriptors were created using SMARTSviewer\\n(smartsview.zbh.uni-hamburg.de,
+        Copyright: ZBH, Center for Bioinformatics Hamburg) de-\\nveloped by Schomburg
+        et al. 132.\\n\\n\\n\\n\\n\\n                                       19the \u2018fruity\u2019
+        scent. There are some exceptions though, like tert-amyl acetate which has a\\n\\n\u2018camphoraceous\u2019
+        rather than \u2018fruity\u2019 scent.140,141\\n\\n   In Seshadri et al. 31,
+        we trained a GNN model to predict the scent of molecules and utilized\\n\\ncounterfactuals9
+        and descriptor explanations10 to quantify scent-structure relationships. The\\n\\nMMACE
+        method was modified to account for the multi-label aspect of scent prediction.
+        This\\n\\nmodification defines molecules that differed from the instance molecule
+        by only the selected\\n\\nscent as counterfactuals. For instance, counterfactuals
+        of the jasmone molecule would be false\\n\\nfor the \u2018jasmine\u2019 scent
+        but would still be positive for \u2018woody,\u2019 \u2018floral\u2019 and \u2018herbal\u2019
+        scents.\\n\\n\\n\\n\\n\\nFigure 5:  Counterfactual for the 2,4 decadienal molecule.
+        \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would
+        result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019
+        scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal
+        is also\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable? [yes/no]\\n\\n\"}]}],\"model\":\"gpt-5-2025-08-07\",\"n\":1,\"temperature\":1.0}"
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-        pages 33-35: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
-        D. White. A perspective on explanations of molecular prediction models. ChemRxiv,
+        pages 3-5: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
+        White. A perspective on explanations of molecular prediction models. ChemRxiv,
         Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02.
-        This article has 1 citations.\\n\\n------------\\n\\n13,\\n\\n     1\u201320.\\n\\n\\n(78)
-        Mastropietro, A.; Pasculli, G.; Feldmann, C.; Rodr\xB4\u0131guez-P\xB4erez,
-        R.; Bajorath, J. Edge-\\n\\n    SHAPer: Bond-Centric Shapley Value-Based Explanation
-        Method for Graph Neural\\n\\n     Networks. iScience 2022, 25, 105043.\\n\\n\\n(79)
-        White, A. D. Deep learning for molecules and materials. Living Journal of Computa-\\n\\n
-        \     tional Molecular Science 2022, 3.\\n\\n(80) \u02D8Strumbelj, E.; Kononenko,
-        I. Explaining prediction models and individual predictions\\n\\n     with feature
-        contributions. Knowledge and Information Systems 2014, 41, 647\u2013665.\\n\\n\\n(81)
-        Erhan, D.; Bengio, Y.; Courville, A.; Vincent, P. Visualizing Higher-Layer Features
-        of\\n\\n     a Deep Network. Technical Report, Univerist\xB4e de Montr\xB4eal
-        2009,\\n\\n\\n(82) Weber, J. K.; Morrone, J. A.; Bagchi, S.; Pabon, J. D.; gu
-        Kang, S.; Zhang, L.;\\n\\n     Cornell, W. D. Simplified, interpretable graph
-        convolutional neural networks for small\\n\\n     molecule activity prediction.
-        Journal of Computer-Aided Molecular Design 2022, 36,\\n\\n     391\u2013404.\\n\\n\\n(83)
-        Riniker, S.; Landrum, G. A. Similarity maps - A visualization strategy for molecular\\n\\n
-        \    fingerprints and machine-learning methods. Journal of Cheminformatics 2013,
-        5, 1\u20137.\\n\\n\\n(84) Humer, C.; Heberle, H.; Montanari, F.; Wolf, T.; Huber,
-        F.; Henderson, R.; Hein-\\n\\n      rich, J.; Streit, M. ChemInformatics Model
-        Explorer (CIME): exploratory analysis of\\n\\n     chemical model explanations.
-        Journal of Cheminformatics 2022, 14, 1\u201314.\\n\\n\\n(85) McGrath, T.; Kapishnikov,
-        A.; Toma\u02C7sev, N.; Pearce, A.; Wattenberg, M.; Hass-\\n\\n      abis, D.;
-        Kim, B.; Paquet, U.; Kramnik, V. Acquisition of chess knowledge in Al-\\n\\n
-        \    phaZero. Proceedings of the National Academy of Sciences 2022, 119, e2206625119.\\n\\n\\n\\n\\n
-        \                                     33(86) Bajusz, D.; R\xB4acz, A.; H\xB4eberger,
-        K. Why is Tanimoto index an appropriate choice for\\n\\n     fingerprint-based
-        similarity calculations? Journal of Cheminformatics 2015, 7, 1\u201313.\\n\\n\\n(87)
-        Huang, Q.; Yamada, M.; Tian, Y.; Singh, D.; Yin, D.; Chang, Y. GraphLIME:\\n\\n
-        \    Local Interpretable Model Explanations for Graph Neural Networks. CoRR
-        2020,\\n\\n     abs/2001.06216.\\n\\n\\n(88) Sokol, K.; Flach, P. A. LIMEtree:
-        Interactively Customisable Explanations Based on\\n\\n     Local Surrogate Multi-output
-        Regression Trees. CoRR 2020, abs/2005.01427.\\n\\n\\n(89) Whitmore, L. S.; George,
-        A.; Hudson, C. M. Mapping chemical performance on molec-\\n\\n     ular structures
-        using locally interpretable explanations. 2016; https://arxiv.org/\\n\\n    abs/1611.07443.\\n\\n\\n(90)
-        Mehdi, S.; Tiwary, P. Thermodynamics of Interpretation. 2022,\\n\\n\\n(91) H\xA8ofler,
-        M. Causal inference based on counterfactuals. BMC Medical Research Method-\\n\\n
-        \    ology 2005, 5, 1\u201312.\\n\\n\\n(92) Woodward, J.; Hitchcock, C. Explanatory
-        Generalizations, Part I: A Counterfactual\\n\\n     Account. No\u02C6us 2003,
-        37, 1\u201324.\\n\\n\\n(93) Frisch, M. F. Theories, models, and explanation;
-        University of California, Berkeley,\\n\\n     1998.\\n\\n\\n(94) Reutlinger,
-        A. Is There A Monist Theory of Causal and Non-Causal Explanations?\\n\\n    The
-        Counterfactual Theory of Scientific Explanation. Philosophy of Science 2016,
-        83,\\n\\n     733\u2013745.\\n\\n\\n(95) Lewis, D. Causation. The journal of
-        philosophy 1974, 70, 556\u2013567.\\n\\n\\n(96) Tanimoto, T. T. Elementary mathematical
-        theory of classification and prediction.\\n\\n     Internal IBM Technical Report
-        1958,\\n\\n\\n                                      34 (97) Rogers, D.; Hahn,
-        M. Extended-Connectivity Fingerprints. Journal of Chemical In-\\n\\n      formation
-        and Modeling 2010, 50, 742\u2013754, PMID: 20426451.\\n\\n\\n (98) Mohapatra,
-        S.; An, J.; G\xB4omez-Bombarelli, R. Chemistry-informed macromolecule\\n\\n
-        \     graph representation for similarity computation, unsupervised and supervised
-        learn-\\n\\n       ing. Machine Learning: Science and Technology 2022, 3, 015028.\\n\\n\\n
-        (99) Doshi-Velez, F.; Kortz, M.; Budish, R.; Bavitz, C.; Gershman, S.; O\u2019Brien,
-        D.;\\n\\n       Scott, K.; Schieber, S.; Waldo, J.; Weinberger, D.; Weller,
-        A.; Wood, A. Account-\\n\\n       ability of AI Under the Law: The Role of Explanation.
-        SSRN Electronic Journal\\n\\n     2017,\\n\\n\\n(100) Wachter, S.; Mittelstadt,
-        B.; Russell, C. Counterfactual explanations without opening\\n\\n      the black
-        box: Automated decisions and the GDPR. Harv. JL & Tech. 2017, 31, 841.\\n\\n\\n(101)
-        Jim\xB4enez-Luna, J.; Grisoni, F.; Schneider, G. Drug discovery with explainable
-        artificial\\n\\n       intelligence. Nature Machine Intelligence 2020 2:10 2020,
-        2, 573\u2013584.\\n\\n\\n(102) Fu, T.; Gao, W.; Xiao, C.; Yasonik, J.; Coley,
-        C. W.; Sun, J. Differentiable Scaffold-\\n\\n      ing Tree for Molecule Optimization.
-        International Conference on Learning Represen-\\n\\n       tations. 2022.\\n\\n\\n(103)
-        Shen, C.; Krenn, M.; Eppel, S.; Aspuru-Guzik, A. Deep molecular dreaming: inverse\\n\\n
-        \    machine learning for de-novo molecular design and interpretability with
-        surjective\\n\\n      representations. Machine Learning: Science and Technology
-        2021, 2, 03LT02.\\n\\n\\n(104) Lucic,  A.;   ter  Hoeve,  M.;   Tolomei,  G.;
-        \  Rijke,  M.;   Silvestri,  F.  CF-\\n\\n     GNNExplainer:  Counterfactual
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+        This article has 1 citations.\\n\\n------------\\n\\n a passive characteristic
+        of a model, whereas explainability\\n\\nis an active characteristic which is
+        used to clarify the internal decision-making process.\\n\\nNamely, an explanation
+        is extra information that gives the context and a cause for one or\\n\\nmore
+        predictions.29 We adopt the same nomenclature in this perspective.\\n\\n   Accuracy
+        and interpretability are two attractive characteristics of DL models. However,\\n\\nDL
+        models are often highly accurate and less interpretable.28,30 XAI provides a
+        way to avoid\\n\\nthat trade-off in chemical property prediction. XAI can be
+        viewed as a two-step process.\\n\\nFirst, we develop an accurate but uninterpretable
+        DL model. Next, we add explanations to\\n\\npredictions. Ideally, if the DL
+        model has correctly learned the input-output relations, then\\n\\nthe explanations
+        should give insight into the underlying mechanism.\\n\\n   In the remainder
+        of this article, we review recent approaches for XAI of chemical property\\n\\nprediction
+        while drawing specific examples from our recent XAI work.9,10,31 We show how\\n\\nin
+        various systems these methods yield explanations that are consistent with known
+        and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n                                       3Theory\\n\\n\\nIn
+        this work, we aim to assemble a common taxonomy for the landscape of XAI while\\n\\nproviding
+        our perspectives. We utilized the vocabulary proposed by Das and Rad 32 to classify\\n\\nXAI.
+        According to their classification, interpretations can be categorized as global
+        or local\\n\\ninterpretations on the basis of \u201Cwhat is being explained?\u201D.
+        For example, counterfactuals are\\n\\nlocal interpretations, as these can explain
+        only a given instance. The second classification is\\n\\nbased on the relation
+        between the model and the interpretation \u2013 is interpretability post-hoc\\n\\n(extrinsic)
+        or intrinsic to the model?.32,33 An intrinsic XAI method is part of the model\\n\\nand
+        is self-explanatory32 These are also referred to as white-box models to contrast
+        them\\n\\nwith non-interpretable black box models.28 An extrinsic method is
+        one that can be applied\\n\\npost-training to any model.33 Post-hoc methods
+        found in the literature focus on interpreting\\n\\nmodels through 1) training
+        data34 and feature attribution,35 2) surrogate models10 and, 3)\\n\\ncounterfactual9
+        or contrastive explanations.36\\n\\n   Often, what is a \u201Cgood\u201D explanation
+        and what are the required components of an ex-\\n\\nplanation are debated.32,37,38
+        Palacio et al. 29 state that the lack of a standard framework\\n\\nhas caused
+        the inability to evaluate the interpretability of a model.  In physical sciences,\\n\\nwe
+        may instead consider if the explanations somehow reflect and expand our understanding\\n\\nof
+        physical phenomena.  For example, Oviedo et al. 39 propose that a model explanation\\n\\ncan
+        be evaluated by considering its agreement with physical observations, which
+        they term\\n\\n\u201Ccorrectness.\u201D For example, if an explanation suggests
+        that polarity affects solubility of a\\n\\nmolecule, and the experimental evidence
+        strengthen the hypothesis, then the explanation\\n\\nis assumed \u201Ccorrect\u201D.
+        In instances where such mechanistic knowledge is sparse, expert bi-\\n\\nases
+        and subjectivity can be used to measure the correctness.40 Other similar metrics
+        of\\n\\ncorrectness such as \u201Cexplanation satisfaction scale\u201D can be
+        found in the literature.41,42 In a\\n\\nrecent study, Humer et al. 43 introduced
+        CIME an interactive web-based tool that allows the\\n\\nusers to inspect model
+        explanations. The aim of this study is to bridge the gap between\\n\\nanalysis
+        of XAI methods. Based on the above discussion, we identify that an agreed upon\\n\\n\\n
+        \                                      4evaluation metric is necessary in XAI.
+        We suggest the following attributes can be used to\\n\\nevaluate explanations.
+        However, the relative importance of each attribute may depend on\\n\\nthe application
+        - actionability may not be as important as faithfulness when evaluating the\\n\\ninterpretability
+        of a static physics based model. Therefore, one can select relative importance\\n\\nof
+        each attribute based on the application.\\n\\n\\n   \u2022 Actionable. Is it
+        clear how we could change the input features to modify the output?\\n\\n\\n
+        \  \u2022 Complete. Does the explanation completely account for the prediction?
+        Did features\\n\\n     not included in the explanation really contribute zero
+        effect to the prediction?44\\n\\n\\n   \u2022 Correct. Does the explanation
+        agree with hypothesized or known underlying physical\\n\\n     mechanism?39\\n\\n\\n
+        \  \u2022 Domain Applicable. Does the explanation use language and concepts
+        of domain ex-\\n\\n      perts?\\n\\n\\n   \u2022 Fidelity/Faithful. Does the
+        explanation agree with the black box model?\\n\\n\\n   \u2022 Robust. Does the
+        explanation change significantly with small changes to the model or\\n\\n      instance
+        being explained?\\n\\n\\n   \u2022 Sparse/Succinct. Is the explanation succinct?\\n\\n\\n
+        \ We present an example evaluation of the SHAP explanation method based on the
+        above\\n\\nattributes.44 Shapley values were proposed as a local explanation
+        method based on feature\\n\\nattribution, as they offer a complete explanation
+        - each feature i\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable?
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-        pages 5-8: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
-        White. A perspective on explanations of molecular prediction models. ChemRxiv,
+        pages 33-35: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        D. White. A perspective on explanations of molecular prediction models. ChemRxiv,
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-        have high\\n\\n\\n                                       5fidelity to the original
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-        depend on \u201Ccomplex human factors and\\n\\napplication scenarios.\u201D37\\n\\n\\nSelf-explaining
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-        regression models and decision trees (DT).\\n\\nIntrinsic models can be found
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-        bioactivity based on the\\n\\nchemical fingerprints.\\n\\n\\n\\n                                                              \u02C6y
-        = \u03A3iWixi                                      (1)\\n\\n\\n   Regularization
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+        This article has 1 citations.\\n\\n------------\\n\\n13,\\n\\n     1\u201320.\\n\\n\\n(78)
+        Mastropietro, A.; Pasculli, G.; Feldmann, C.; Rodr\xB4\u0131guez-P\xB4erez,
+        R.; Bajorath, J. Edge-\\n\\n    SHAPer: Bond-Centric Shapley Value-Based Explanation
+        Method for Graph Neural\\n\\n     Networks. iScience 2022, 25, 105043.\\n\\n\\n(79)
+        White, A. D. Deep learning for molecules and materials. Living Journal of Computa-\\n\\n
+        \     tional Molecular Science 2022, 3.\\n\\n(80) \u02D8Strumbelj, E.; Kononenko,
+        I. Explaining prediction models and individual predictions\\n\\n     with feature
+        contributions. Knowledge and Information Systems 2014, 41, 647\u2013665.\\n\\n\\n(81)
+        Erhan, D.; Bengio, Y.; Courville, A.; Vincent, P. Visualizing Higher-Layer Features
+        of\\n\\n     a Deep Network. Technical Report, Univerist\xB4e de Montr\xB4eal
+        2009,\\n\\n\\n(82) Weber, J. K.; Morrone, J. A.; Bagchi, S.; Pabon, J. D.; gu
+        Kang, S.; Zhang, L.;\\n\\n     Cornell, W. D. Simplified, interpretable graph
+        convolutional neural networks for small\\n\\n     molecule activity prediction.
+        Journal of Computer-Aided Molecular Design 2022, 36,\\n\\n     391\u2013404.\\n\\n\\n(83)
+        Riniker, S.; Landrum, G. A. Similarity maps - A visualization strategy for molecular\\n\\n
+        \    fingerprints and machine-learning methods. Journal of Cheminformatics 2013,
+        5, 1\u20137.\\n\\n\\n(84) Humer, C.; Heberle, H.; Montanari, F.; Wolf, T.; Huber,
+        F.; Henderson, R.; Hein-\\n\\n      rich, J.; Streit, M. ChemInformatics Model
+        Explorer (CIME): exploratory analysis of\\n\\n     chemical model explanations.
+        Journal of Cheminformatics 2022, 14, 1\u201314.\\n\\n\\n(85) McGrath, T.; Kapishnikov,
+        A.; Toma\u02C7sev, N.; Pearce, A.; Wattenberg, M.; Hass-\\n\\n      abis, D.;
+        Kim, B.; Paquet, U.; Kramnik, V. Acquisition of chess knowledge in Al-\\n\\n
+        \    phaZero. Proceedings of the National Academy of Sciences 2022, 119, e2206625119.\\n\\n\\n\\n\\n
+        \                                     33(86) Bajusz, D.; R\xB4acz, A.; H\xB4eberger,
+        K. Why is Tanimoto index an appropriate choice for\\n\\n     fingerprint-based
+        similarity calculations? Journal of Cheminformatics 2015, 7, 1\u201313.\\n\\n\\n(87)
+        Huang, Q.; Yamada, M.; Tian, Y.; Singh, D.; Yin, D.; Chang, Y. GraphLIME:\\n\\n
+        \    Local Interpretable Model Explanations for Graph Neural Networks. CoRR
+        2020,\\n\\n     abs/2001.06216.\\n\\n\\n(88) Sokol, K.; Flach, P. A. LIMEtree:
+        Interactively Customisable Explanations Based on\\n\\n     Local Surrogate Multi-output
+        Regression Trees. CoRR 2020, abs/2005.01427.\\n\\n\\n(89) Whitmore, L. S.; George,
+        A.; Hudson, C. M. Mapping chemical performance on molec-\\n\\n     ular structures
+        using locally interpretable explanations. 2016; https://arxiv.org/\\n\\n    abs/1611.07443.\\n\\n\\n(90)
+        Mehdi, S.; Tiwary, P. Thermodynamics of Interpretation. 2022,\\n\\n\\n(91) H\xA8ofler,
+        M. Causal inference based on counterfactuals. BMC Medical Research Method-\\n\\n
+        \    ology 2005, 5, 1\u201312.\\n\\n\\n(92) Woodward, J.; Hitchcock, C. Explanatory
+        Generalizations, Part I: A Counterfactual\\n\\n     Account. No\u02C6us 2003,
+        37, 1\u201324.\\n\\n\\n(93) Frisch, M. F. Theories, models, and explanation;
+        University of California, Berkeley,\\n\\n     1998.\\n\\n\\n(94) Reutlinger,
+        A. Is There A Monist Theory of Causal and Non-Causal Explanations?\\n\\n    The
+        Counterfactual Theory of Scientific Explanation. Philosophy of Science 2016,
+        83,\\n\\n     733\u2013745.\\n\\n\\n(95) Lewis, D. Causation. The journal of
+        philosophy 1974, 70, 556\u2013567.\\n\\n\\n(96) Tanimoto, T. T. Elementary mathematical
+        theory of classification and prediction.\\n\\n     Internal IBM Technical Report
+        1958,\\n\\n\\n                                      34 (97) Rogers, D.; Hahn,
+        M. Extended-Connectivity Fingerprints. Journal of Chemical In-\\n\\n      formation
+        and Modeling 2010, 50, 742\u2013754, PMID: 20426451.\\n\\n\\n (98) Mohapatra,
+        S.; An, J.; G\xB4omez-Bombarelli, R. Chemistry-informed macromolecule\\n\\n
+        \     graph representation for similarity computation, unsupervised and supervised
+        learn-\\n\\n       ing. Machine Learning: Science and Technology 2022, 3, 015028.\\n\\n\\n
+        (99) Doshi-Velez, F.; Kortz, M.; Budish, R.; Bavitz, C.; Gershman, S.; O\u2019Brien,
+        D.;\\n\\n       Scott, K.; Schieber, S.; Waldo, J.; Weinberger, D.; Weller,
+        A.; Wood, A. Account-\\n\\n       ability of AI Under the Law: The Role of Explanation.
+        SSRN Electronic Journal\\n\\n     2017,\\n\\n\\n(100) Wachter, S.; Mittelstadt,
+        B.; Russell, C. Counterfactual explanations without opening\\n\\n      the black
+        box: Automated decisions and the GDPR. Harv. JL & Tech. 2017, 31, 841.\\n\\n\\n(101)
+        Jim\xB4enez-Luna, J.; Grisoni, F.; Schneider, G. Drug discovery with explainable
+        artificial\\n\\n       intelligence. Nature Machine Intelligence 2020 2:10 2020,
+        2, 573\u2013584.\\n\\n\\n(102) Fu, T.; Gao, W.; Xiao, C.; Yasonik, J.; Coley,
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-        pages 3-5: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
+        pages 5-8: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
         White. A perspective on explanations of molecular prediction models. ChemRxiv,
         Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02, doi:10.26434/chemrxiv-2022-qfv02.
-        This article has 1 citations.\\n\\n------------\\n\\n a passive characteristic
-        of a model, whereas explainability\\n\\nis an active characteristic which is
-        used to clarify the internal decision-making process.\\n\\nNamely, an explanation
-        is extra information that gives the context and a cause for one or\\n\\nmore
-        predictions.29 We adopt the same nomenclature in this perspective.\\n\\n   Accuracy
-        and interpretability are two attractive characteristics of DL models. However,\\n\\nDL
-        models are often highly accurate and less interpretable.28,30 XAI provides a
-        way to avoid\\n\\nthat trade-off in chemical property prediction. XAI can be
-        viewed as a two-step process.\\n\\nFirst, we develop an accurate but uninterpretable
-        DL model. Next, we add explanations to\\n\\npredictions. Ideally, if the DL
-        model has correctly learned the input-output relations, then\\n\\nthe explanations
-        should give insight into the underlying mechanism.\\n\\n   In the remainder
-        of this article, we review recent approaches for XAI of chemical property\\n\\nprediction
-        while drawing specific examples from our recent XAI work.9,10,31 We show how\\n\\nin
-        various systems these methods yield explanations that are consistent with known
-        and\\n\\nmechanisms in structure-property relationships.\\n\\n\\n\\n\\n\\n                                       3Theory\\n\\n\\nIn
-        this work, we aim to assemble a common taxonomy for the landscape of XAI while\\n\\nproviding
-        our perspectives. We utilized the vocabulary proposed by Das and Rad 32 to classify\\n\\nXAI.
-        According to their classification, interpretations can be categorized as global
-        or local\\n\\ninterpretations on the basis of \u201Cwhat is being explained?\u201D.
-        For example, counterfactuals are\\n\\nlocal interpretations, as these can explain
-        only a given instance. The second classification is\\n\\nbased on the relation
-        between the model and the interpretation \u2013 is interpretability post-hoc\\n\\n(extrinsic)
-        or intrinsic to the model?.32,33 An intrinsic XAI method is part of the model\\n\\nand
-        is self-explanatory32 These are also referred to as white-box models to contrast
-        them\\n\\nwith non-interpretable black box models.28 An extrinsic method is
-        one that can be applied\\n\\npost-training to any model.33 Post-hoc methods
-        found in the literature focus on interpreting\\n\\nmodels through 1) training
-        data34 and feature attribution,35 2) surrogate models10 and, 3)\\n\\ncounterfactual9
-        or contrastive explanations.36\\n\\n   Often, what is a \u201Cgood\u201D explanation
-        and what are the required components of an ex-\\n\\nplanation are debated.32,37,38
-        Palacio et al. 29 state that the lack of a standard framework\\n\\nhas caused
-        the inability to evaluate the interpretability of a model.  In physical sciences,\\n\\nwe
-        may instead consider if the explanations somehow reflect and expand our understanding\\n\\nof
-        physical phenomena.  For example, Oviedo et al. 39 propose that a model explanation\\n\\ncan
-        be evaluated by considering its agreement with physical observations, which
-        they term\\n\\n\u201Ccorrectness.\u201D For example, if an explanation suggests
-        that polarity affects solubility of a\\n\\nmolecule, and the experimental evidence
-        strengthen the hypothesis, then the explanation\\n\\nis assumed \u201Ccorrect\u201D.
-        In instances where such mechanistic knowledge is sparse, expert bi-\\n\\nases
-        and subjectivity can be used to measure the correctness.40 Other similar metrics
-        of\\n\\ncorrectness such as \u201Cexplanation satisfaction scale\u201D can be
-        found in the literature.41,42 In a\\n\\nrecent study, Humer et al. 43 introduced
-        CIME an interactive web-based tool that allows the\\n\\nusers to inspect model
-        explanations. The aim of this study is to bridge the gap between\\n\\nanalysis
-        of XAI methods. Based on the above discussion, we identify that an agreed upon\\n\\n\\n
-        \                                      4evaluation metric is necessary in XAI.
-        We suggest the following attributes can be used to\\n\\nevaluate explanations.
-        However, the relative importance of each attribute may depend on\\n\\nthe application
-        - actionability may not be as important as faithfulness when evaluating the\\n\\ninterpretability
-        of a static physics based model. Therefore, one can select relative importance\\n\\nof
-        each attribute based on the application.\\n\\n\\n   \u2022 Actionable. Is it
-        clear how we could change the input features to modify the output?\\n\\n\\n
-        \  \u2022 Complete. Does the explanation completely account for the prediction?
-        Did features\\n\\n     not included in the explanation really contribute zero
-        effect to the prediction?44\\n\\n\\n   \u2022 Correct. Does the explanation
-        agree with hypothesized or known underlying physical\\n\\n     mechanism?39\\n\\n\\n
-        \  \u2022 Domain Applicable. Does the explanation use language and concepts
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-        explanation change significantly with small changes to the model or\\n\\n      instance
-        being explained?\\n\\n\\n   \u2022 Sparse/Succinct. Is the explanation succinct?\\n\\n\\n
-        \ We present an example evaluation of the SHAP explanation method based on the
-        above\\n\\nattributes.44 Shapley values were proposed as a local explanation
-        method based on feature\\n\\nattribution, as they offer a complete explanation
-        - each feature i\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable?
-        [yes/no]\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        This article has 1 citations.\\n\\n------------\\n\\nnct?\\n\\n\\n  We present
+        an example evaluation of the SHAP explanation method based on the above\\n\\nattributes.44
+        Shapley values were proposed as a local explanation method based on feature\\n\\nattribution,
+        as they offer a complete explanation - each feature is assigned a fraction of\\n\\nthe
+        prediction value.44,45 Completeness is a clearly measurable and well-defined
+        metric, but\\n\\nyields explanations with many components. Yet Shapley values
+        are not actionable nor sparse.\\n\\nThey are non-sparse as every feature has
+        a non-zero attribution and not-actionable because\\n\\nthey do not provide a
+        set of features which changes the outcome.46 Ribeiro et al. 35 proposed\\n\\na
+        surrogate model method that aims to provide sparse/succinct explanations that
+        have high\\n\\n\\n                                       5fidelity to the original
+        model. In Wellawatte et al. 9 we argue that counterfactuals are \u201Cbet-\\n\\nter\u201D
+        explanations because they are actionable and sparse. We highlight that, evaluation
+        of\\n\\nexplanations is a difficult task because explanations are fundamentally
+        for and by humans.\\n\\nTherefore, these evaluations are subjective, as they
+        depend on \u201Ccomplex human factors and\\n\\napplication scenarios.\u201D37\\n\\n\\nSelf-explaining
+        models\\n\\nA self-explanatory model is one that is intrinsically interpretable
+        to an expert.47 Two com-\\n\\nmon examples found in the literature are linear
+        regression models and decision trees (DT).\\n\\nIntrinsic models can be found
+        in other XAI applications acting as surrogate models (proxy\\n\\nmodels) due
+        to their transparent nature.48,49 A linear model is described by the equation\\n\\n1
+        where, W\u2019s are the weight parameters and x\u2019s are the input features
+        associated with the\\n\\nprediction \u02C6y. Therefore, we observe that the
+        weights can be used to derive a complete expla-\\n\\nnation of the model - trained
+        weights quantify the importance of each feature.47 DT models\\n\\nare another
+        type of self-explaining models which have been used in classification and high-\\n\\nthroughput
+        screening tasks.  Gajewicz et al. 50 used DT models to classify nanomaterials\\n\\nthat
+        identify structural features responsible for surface activity. In another study
+        by Han\\n\\net al. 51, a DT model was developed to filter compounds by their
+        bioactivity based on the\\n\\nchemical fingerprints.\\n\\n\\n\\n                                                              \u02C6y
+        = \u03A3iWixi                                      (1)\\n\\n\\n   Regularization
+        techniques such as EXPO52 and RRR53 are designed to enhance the black-\\n\\nbox
+        model interpretability.54 Although one can argue that \u201Csimplicity\u201D
+        of models are posi-\\n\\ntively correlated with interpretability, this is based
+        on how the interpretability is evaluated.\\n\\nFor example, Lipton 55 argue
+        that, from the notion of \u201Csimulatability\u201D (the degree to which a\\n\\nhuman
+        can predict the outcome based on inputs), self-explanatory linear models, rule-based\\n\\n\\n\\n
+        \                                      6systems, and DT\u2019s can be claimed
+        uninterpretable. A human can predict the outcome given\\n\\nthe inputs only
+        if the input features are interpretable. Therefore, a linear model which takes\\n\\nin
+        non-descriptive inputs may not be as transparent. On the other hand, a linear
+        model\\n\\nis not innately accurate as they fail to capture non-linear relationships
+        in data, limiting is\\n\\napplicability. Similarly, a DT is a rule-based model
+        and lacks physics informed knowledge.\\n\\nTherefore, an existing drawback is
+        the trade-offbetween the degree of understandability and\\n\\nthe accuracy of
+        a model. For example, an intrinsic model (linear regression or decision trees)\\n\\ncan
+        be described through the trainable parameters, but it may fail to \u201Ccorrectly\u201D
+        capture\\n\\nnon-linear relations in the data.\\n\\n\\nAttribution methods\\n\\n\\nFeature
+        attribution methods explain black box predictions by assigning each input feature\\n\\na
+        numerical value, which indicates its importance or contribution to the prediction.
+        Feature\\n\\nattributions provide local explanations, but can be averaged or
+        combined to explain multi-\\n\\nple instances. Atom-based numerical assignments
+        are commonly referred to as heatmaps.56\\n\\nSheridan 57 describes an atom-wise
+        attribution method for interpreting QSAR models. Re-\\n\\ncently, Rasmussen
+        et al. 58 showed that Crippen logP models serve as a benchmark for\\n\\nheatmap
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+        are gradient based methods,59,60 Shapley Additive exPlanations (SHAP),44 and
+        layer-\\n\\nwise relevance prorogation.61\\n\\n   Gradient based approaches
+        are based on the hypothesis that gradients for neural net-\\n\\nworks are analogous
+        to coefficients for regression models.62 Class activation maps (CAM),63\\n\\ngradCAM,64
+        smoothGrad,,65 and integrated gradients62 are examples of this method. The\\n\\nmain
+        idea behind feature attributions with gradients can be represented with equation
+        \ 2.\\n\\n                                          \u2206\u02C6f(\u20D7x) \u2248\u2202\u02C6f(\u20D7x)
+        \                                   (2)\\n                               \u2206xi
+        \     \u2202xi\\n\\n\\n\\n                                       7                                  \\n\\n------------\\n\\nQuestion:
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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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-        from wellawatteUnknownyearaperspectiveon pages 16-20: Geemi P. Wellawatte, Heta
+        "{\"messages\":[{\"role\":\"system\",\"content\":\"Answer in a direct and
+        concise tone. Your audience is an expert, so be highly specific. If there are
+        ambiguous terms or acronyms, first define them.\"},{\"role\":\"user\",\"content\":\"Answer
+        the question below with the context.\\n\\nContext:\\n\\npqac-cddc4981: The excerpt
+        defines counterfactual explanations as inputs altered to produce a different
+        model prediction and states that they provide local (instance-level), actionable
+        explanations. Actionability means the explanation suggests which features can
+        be changed to modify the outcome\u2014for example, replacing a hydrophobic functional
+        group with a hydrophilic one to increase solubility. Thus, counterfactuals not
+        only interpret model behavior but also indicate concrete feature-level changes
+        that could achieve a desired prediction shift.\\nFrom Geemi P. Wellawatte, Heta
         A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
         of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02,
-        doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\n------------\\n\\nssion
-        challenge and is\\n\\nimportant for chemical process design, drug design and
-        crystallization.133\u2013136 In our previous\\n\\nworks,9,10 we implemented
-        and trained an RNN model in Keras to predict solubilities (log\\n\\nmolarity)
-        of small molecules.127 The AqSolDB curated database137 was used to train the\\n\\nRNN
-        model.\\n\\n   In this task, counterfactuals are based on equation 6. Figure
-        3 illustrates the generated\\n\\nlocal chemical space and the top four counterfactuals.
-        Based on the counterfactuals, we ob-\\n\\nserve that the modifications to the
-        ester group and other heteroatoms play an important role\\n\\nin solubility.
-        These findings align with known experimental and basic chemical intuition.134\\n\\nFigure
-        4 shows a quantitative measurement of how substructures are contributing to
-        the pre-\\n\\n\\n\\n                                       16Figure 2: Descriptor
-        explanations along with natural language explanation obtained for BBB\\npermeability
-        of Alprozolam molecule. The green and red bars show descriptors that influ-\\nence
-        predictions positively and negatively, respectively. Dotted yellow lines show
-        significance\\nthreshold (\u03B1 = 0.05) for the t-statistic. Molecular descriptors
-        show molecule-level proper-\\nties that are important for the prediction. ECFP
-        and MACCS descriptors indicate which\\nsubstructures influence model predictions.
-        MACCS explanations lead to text explanations\\nas shown. Republished from Ref.10
-        with permission from authors. SMARTS annotations for\\nMACCS descriptors were
-        created using SMARTSviewer (smartsview.zbh.uni-hamburg.de,\\nCopyright: ZBH,
-        Center for Bioinformatics Hamburg) developed by Schomburg et al. 132.\\n\\n\\n\\n\\n\\n
-        \                                      17diction. For example, we see that adding
-        acidic and basic groups as well as hydrogen bond\\n\\nacceptors, increases solubility.
-        Substructure importance from ECFP97 and MACCS138 de-\\n\\nscriptors indicate
-        that adding heteroatoms increases solubility, while adding rings structures\\n\\nmakes
-        the molecule less soluble. Although these are established hypotheses, it is
-        interesting\\n\\nto see they can be derived purely from the data via DL and
-        XAI.\\n\\n\\n\\n\\n\\nFigure 3: Generated chemical space for solubility prediction
-        using the RNN model. The\\nchemical space is a 2D projection of the pairwise
-        Tanimoto similarities of the local coun-\\nterfactuals. Each data point is colored
-        by solubility. Top 4 counterfactuals are shown here.\\nRepublished from Ref.9
-        with permission from the Royal Society of Chemistry.\\n\\n\\n\\nGeneralizing
-        XAI \u2013 interpreting scent-structure relationships\\n\\n\\nIn this example,
-        we show how non-local structure-property relationships can be learned with\\n\\nXAI
-        across multiple molecules. Molecular scent prediction is a multi-label classification
-        task\\n\\nbecause a molecule can be described by more than one scent. For example,
-        the molecule\\n\\njasmone can be described as having \u2018jasmine,\u2019 \u2018woody,\u2019
-        \u2018floral,\u2019 and \u2019herbal\u2019 scents.139 The\\n\\nscent-structure
-        relationship is not very well understood,140 although some relationships are\\n\\nknown.
-        \ For example, molecules with an ester functional group are often associated
-        with\\n\\n\\n                                       18Figure 4: Descriptor explanations
-        for solubility prediction model. The green and red bars\\nshow descriptors that
-        influence predictions positively and negatively, respectively. Dotted\\nyellow
-        lines show significance threshold (\u03B1 = 0.05) for the t-statistic. The MACCS
-        and\\nECFP descriptors indicate which substructures influence model predictions.
-        MACCS sub-\\nstructures may either be present in the molecule as is or may represent
-        a modification. ECFP\\nfingerprints are substructures in the molecule that affect
-        the prediction. MACCS descriptor\\nare used to obtain text explanations as shown.
-        Republished from Ref.10 with permission from\\nauthors. SMARTS annotations for
-        MACCS descriptors were created using SMARTSviewer\\n(smartsview.zbh.uni-hamburg.de,
-        Copyright: ZBH, Center for Bioinformatics Hamburg) de-\\nveloped by Schomburg
-        et al. 132.\\n\\n\\n\\n\\n\\n                                       19the \u2018fruity\u2019
-        scent. There are some exceptions though, like tert-amyl acetate which has a\\n\\n\u2018camphoraceous\u2019
-        rather than \u2018fruity\u2019 scent.140,141\\n\\n   In Seshadri et al. 31,
-        we trained a GNN model to predict the scent of molecules and utilized\\n\\ncounterfactuals9
-        and descriptor explanations10 to quantify scent-structure relationships. The\\n\\nMMACE
-        method was modified to account for the multi-label aspect of scent prediction.
-        This\\n\\nmodification defines molecules that differed from the instance molecule
-        by only the selected\\n\\nscent as counterfactuals. For instance, counterfactuals
-        of the jasmone molecule would be false\\n\\nfor the \u2018jasmine\u2019 scent
-        but would still be positive for \u2018woody,\u2019 \u2018floral\u2019 and \u2018herbal\u2019
-        scents.\\n\\n\\n\\n\\n\\nFigure 5:  Counterfactual for the 2,4 decadienal molecule.
-        \ The counterfactual indicates\\nstructural changes to ethyl benzoate that would
-        result in the model predicting the molecule\\nto not contain the \u2018fruity\u2019
-        scent. The Tanimoto96 similarity between the counterfactual and\\n2,4 decadienal
-        is also\\n\\n------------\\n\\nQuestion: Are counterfactuals actionable? [yes/no]\\n\\n\"}]}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
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-        solubility, aligning with chemical intuition. Similarly, counterfactuals for
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-        These findings suggest that counterfactuals can guide actionable decisions in
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-        a base molecule to achieve contrasting chemical properties, making them useful
-        for understanding and modifying molecular predictions.\nFrom Geemi P. Wellawatte,
-        Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective on explanations
-        of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02,
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-        are actionable and sparse. Actionable explanations provide a set of features
-        that can change the outcome, making them useful for decision-making. This contrasts
-        with Shapley values, which are non-sparse and not actionable.\nFrom Geemi P.
+        doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\npqac-a555c6c5:
+        The article applies Molecular Model Agnostic Counterfactual Explanations (MMACE)
+        to a blood\u2013brain barrier (BBB) permeation model. For a molecule predicted
+        not to permeate, counterfactuals modify the carboxylic acid group, flipping
+        the prediction to permeable. The authors state this demonstrates the advantage
+        of counterfactual explanations because they suggest actionable modifications
+        to make the molecule cross the BBB. These changes align with mechanistic factors
+        (increased hydrophobic interactions and surface area). More broadly, counterfactuals,
+        akin to matched molecular pairs, help uncover structure\u2013property relationships
+        and propose concrete structural edits.\\nFrom Geemi P. Wellawatte, Heta A. Gandhi,
+        Aditi Seshadri, and Andrew D. White. A perspective on explanations of molecular
+        prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02,
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+        analyses concur (acidic/basic groups and H\u2011bond acceptors increase solubility;
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+        e.g., edits that make a molecule lose the \u2018fruity\u2019 scent while retaining
+        others. These examples show counterfactuals provide targeted structure changes
+        aligned with chemical intuition, usable for molecular design.\\nFrom Geemi P.
         Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D. White. A perspective
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+        This article has 1 citations.\\n\\npqac-28027630: The excerpt defines a molecular
+        counterfactual as a molecule with minimal distance from a base molecule but
+        with contrasting properties, using Tanimoto similarity of ECFP4 fingerprints
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+        because they are represented as chemical structures familiar to chemists, are
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+        doi:10.26434/chemrxiv-2022-qfv02. This article has 1 citations.\\n\\npqac-9c607584:
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+        of molecular prediction models. ChemRxiv, Unknown year. URL: https://doi.org/10.26434/chemrxiv-2022-qfv02,
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diff --git a/tests/test_paperqa.py b/tests/test_paperqa.py
index 034316701..568b168d2 100644
--- a/tests/test_paperqa.py
+++ b/tests/test_paperqa.py
@@ -1732,8 +1732,9 @@ async def test_images_corrupt(stub_data_dir: Path, caplog) -> None:
         "What districts neighbor the Western Addition?", settings=settings
     )
     assert not session.contexts, "Expected no contexts to be made from a bad image."
-    assert (
-        "unsupported image" in caplog.text
+    assert any(
+        x in caplog.text.lower()
+        for x in ("unsupported image", "could not process image")
     ), "Expected a caught exception about an unsupported image."
 
     # By suppressing the use of images, we can actually gather evidence now
@@ -1817,6 +1818,7 @@ async def test_pre_prompt(stub_data_dir: Path) -> None:
     pre = "What is water's boiling point in Fahrenheit? Please respond with a complete sentence."
 
     settings = Settings.from_name("fast")
+    settings.llm = "gpt-4o-2024-11-20"
     settings.prompts.pre = pre
     docs = Docs()
     await docs.aadd(