diff --git a/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml b/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml
index 42d97672b..5a4029b2e 100644
--- a/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml
+++ b/tests/cassettes/test_get_reasoning[openrouter-deepseek].yaml
@@ -46,27 +46,27 @@ interactions:
         Content-Type:
           - application/json
         Date:
-          - Sat, 15 Nov 2025 00:43:41 GMT
+          - Tue, 18 Nov 2025 01:09:23 GMT
         Via:
-          - 1.1 b0ed8f5cd57ec24ce179421915a813d6.cloudfront.net (CloudFront)
+          - 1.1 edc643c7c426bec36e205453aa531064.cloudfront.net (CloudFront)
         X-Amz-Cf-Id:
-          - Bv-CndpheR8NYCPHPc0s-cSF5lEv8u0cGBGOOPTZaKzeTCmZgg7lZg==
+          - WINwDw8u2pStByuMg4aU_oZSJbqVNGbHnoU5qPR8D_2SpkOZUkDuLg==
         X-Amz-Cf-Pop:
           - SFO53-P7
         X-Cache:
           - Miss from cloudfront
         x-amz-apigw-id:
-          - UDztuFZzvHcErpQ=
+          - UNwSmHpAvHcECDw=
         x-amzn-Remapped-Connection:
           - keep-alive
         x-amzn-Remapped-Content-Length:
           - "1398"
         x-amzn-Remapped-Date:
-          - Sat, 15 Nov 2025 00:43:41 GMT
+          - Tue, 18 Nov 2025 01:09:23 GMT
         x-amzn-Remapped-Server:
           - gunicorn
         x-amzn-RequestId:
-          - ba489209-d5bd-47b9-8176-e6b810bbedb6
+          - d0487bf1-ebba-46d5-b479-0c5b91c3123d
       status:
         code: 200
         message: OK
@@ -88,105 +88,105 @@ interactions:
     response:
       body:
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-        It is a tool for explainable AI (XAI) in chemistry, helping to interpret the
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-        pages 3-5: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew D.
-        White. A perspective on explanations of molecular prediction models. Journal
+        pages 25-28: 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 55 citations and is from a domain
-        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
-        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: What is XAI?\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        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,
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+        of Sciences of the United States of America 2019, 116, 22071\u201322080.\\n\\n\\n(27)
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+        Program.\\n\\n    AI Magazine 2019, 40, 44\u201358.\\n\\n\\n(28) Biran, O.;
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+        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
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-        pages 20-22: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
-        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.
+        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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-        leading peer-reviewed journal.\\n\\n---\\n\\nnal molecule.  The counterfactual
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-        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
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-        four\\n\\n\\n                                       20bonds, and a C=O double
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-        For the \u2018pineapple\u2019 scent, the following natural language explanation
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-        O group, and C=O double bond, as well as the absence of\\n\\nan Aromatic atom.\u201D31
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-        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
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-        whose input is a molecule. These two methods can be applied for both classification\\n\\nand
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-        counterfactual is one with a minimal distance from a base molecular, but\\n\\nwith
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-        future.\\n\\nCounterfactual explanations are useful because they are represented
-        as chemical structures\\n\\n(familiar to domain experts), sparse, and are actionable.
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-        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,
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-        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
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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
+        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
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+        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
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+        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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         0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or
         may not contain relevant information. If not, leave `summary` empty, and make
         `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatte2023aperspectiveon
-        pages 25-28: Geemi P. Wellawatte, Heta A. Gandhi, Aditi Seshadri, and Andrew
+        pages 22-25: 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 55 citations and is from a domain
-        leading peer-reviewed journal.\\n\\n---\\n\\n2021, 25, 1315\u20131360.\\n\\n\\n
+        leading peer-reviewed journal.\\n\\n---\\n\\nut 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\\nthe correct underlying
+        chemical principles. We also showed that black-box modeling first,\\n\\nfollowed
+        by XAI, is a path to structure-property relationships without needing to trade\\n\\nbetween
+        accuracy and interpretability. However, XAI in chemistry has some major open\\n\\nquestions,
+        that are also related to the black-box nature of the deep learning. Some are\\n\\n\\n\\n
+        \                                      22highlighted below:\\n\\n\\n   \u2022
+        Explanation representation: How is an explanation presented \u2013 text, a molecule,
+        attri-\\n\\n      butions, a concept, etc?\\n\\n\\n   \u2022 Molecular distance:
+        \ in XAI approaches such as counterfactual generation, the \u201Cdis-\\n\\n
+        \    tance\u201D between two molecules is minimized. Molecular distance is subjective.
+        Possibil-\\n\\n       ities are distance based on molecular properties, synthesis
+        routes, and direct structure\\n\\n     comparisons.\\n\\n\\n   \u2022 Regulations:
+        As black-box models move from research to industry, healthcare, and\\n\\n     environmental
+        settings, we expect XAI to become more important to explain decisions\\n\\n
+        \     to chemists or non-experts and possibly be legally required. Explanations
+        may need\\n\\n      to be tuned for be for doctors instead of chemists or to
+        satisfy a legal requirement.\\n\\n\\n   \u2022 Chemical space: Chemical space
+        is the set of molecules that are realizable; \u201Crealiz-\\n\\n     able\u201D
+        can be defined from purchasable to synthesizable to satisfied valences. What
+        is\\n\\n     most useful? Can an explanation consider nearby impossible molecules?
+        How can we\\n\\n     generate local chemical spaces centered around a specific
+        molecule for finding counter-\\n\\n      factuals or other instance explanations?
+        \ Similarly, can \u201Cactivity cliffs\u201D be connected\\n\\n      to explanations
+        and the local chemical space.149\\n\\n\\n   \u2022 Evaluating XAI : there is
+        a lack of a systematic framework (quantitative or qualitative)\\n\\n      to
+        evaluate correctness and applicability of an explanation. Can there be a universal\\n\\n
+        \    framework, or should explanations be chosen and evaluated based on the
+        audience and\\n\\n     domain? For example, work by Rasmussen et al. 58 attempts
+        to focus on comparing\\n\\n      feature attribution XAI methods via Crippen\u2019s
+        logP scores.\\n\\n\\n\\n\\n\\n                                       23Acknowledgements\\n\\n\\nResearch
+        reported in this work was supported by the National Institute of General Medical\\n\\nSciences
+        of the National Institutes of Health under award number R35GM137966. This work\\n\\nwas
+        supported by the NSF under awards 1751471 and 1764415. We thank the Center for\\n\\nIntegrated
+        Research Computing at the University of Rochester for providing computational\\n\\nresources.\\n\\n\\nReferences\\n\\n\\n
+        \ (1) Choudhary, K.; DeCost, B.; Chen, C.; Jain, A.; Tavazza, F.; Cohn, R.;
+        Park, C. W.;\\n\\n     Choudhary, A.; Agrawal, A.; Billinge, S. J.; Holm, E.;
+        Ong, S. P.; Wolverton, C.\\n\\n      Recent advances and applications of deep
+        learning methods in materials science. npj\\n\\n      Computational Materials
+        2022, 8.\\n\\n\\n  (2) Keith, J. A.; Vassilev-Galindo, V.; Cheng, B.; Chmiela,
+        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.;
+        Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R. K.; Kumar, P. Artifi-\\n\\n
+        \     cial intelligence to deep learning: machine intelligence approach for
+        drug discovery.\\n\\n     Molecular diversity 2021, 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
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-        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
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-        Nature\\n\\n     communications 2019, 10, 1\u20138.\\n\\n\\n(21) DeGrave, A.
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-        Explaining the\\n\\n     predictions of any classifier. Proceedings of the 22nd
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-        logP scores.\\n\\n\\n\\n\\n\\n                                       23Acknowledgements\\n\\n\\nResearch
-        reported in this work was supported by the National Institute of General Medical\\n\\nSciences
-        of the National Institutes of Health under award number R35GM137966. This work\\n\\nwas
-        supported by the NSF under awards 1751471 and 1764415. We thank the Center for\\n\\nIntegrated
-        Research Computing at the University of Rochester for providing computational\\n\\nresources.\\n\\n\\nReferences\\n\\n\\n
-        \ (1) Choudhary, K.; DeCost, B.; Chen, C.; Jain, A.; Tavazza, F.; Cohn, R.;
-        Park, C. W.;\\n\\n     Choudhary, A.; Agrawal, A.; Billinge, S. J.; Holm, E.;
-        Ong, S. P.; Wolverton, C.\\n\\n      Recent advances and applications of deep
-        learning methods in materials science. npj\\n\\n      Computational Materials
-        2022, 8.\\n\\n\\n  (2) Keith, J. A.; Vassilev-Galindo, V.; Cheng, B.; Chmiela,
-        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.;
-        Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R. K.; Kumar, P. Artifi-\\n\\n
-        \     cial intelligence to deep learning: machine intelligence approach for
-        drug discovery.\\n\\n     Molecular diversity 2021, 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-ac\\n\\n---\\n\\nQuestion:
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-        \                                         \u2206\u02C6f(\u20D7x) \u2248\u2202\u02C6f(\u20D7x)
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-        \     \u2202xi\\n\\n\\n\\n                                       7                                                                     \u2206\u02C6f(\u20D7x)
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-        The left-                                                 \u2206xi\\n\\nhand
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-        is\\n\\napproximated.\\n\\n   Gradient based explanations have been widely used
-        to interpret chemistry predictions.60,66\u201370\\n\\nMcCloskey et al. 60 used
-        graph convolutional networks (GCNs) to predict protein-ligand\\n\\nbinding and
-        explained the binding logic for these predictions using integrated gradients.\\n\\nPope
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-        neural networks (GNNs).\\n\\nSanchez-Lengeling et al. 68 present comprehensive,
-        open-source XAI benchmarks to explain\\n\\nGNNs and other graph based models.
-        They compare the performance of class activation\\n\\nmaps (CAM),63 gradCAM,64
-        smoothGrad,,65 integrated gradients62 and attention mecha-\\n\\nnisms for explaining
-        outcomes of classification as well as regression tasks. They concluded\\n\\nthat
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-        cliffs\\n\\nand concluded that gradCAM and integrated gradients provided the
-        most interpretability\\n\\nfor GNNs.  The GNNExplainer69  is an approach for
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-        method focuses on identifying which sub-graphs con-\\n\\ntribute most to the
-        prediction by maximizing mutual information between the prediction\\n\\nand
-        distribution of all possible sub-graphs. Ying et al. 69 show that GNNExplainer
-        can be\\n\\nused to obtain model-agnostic explanations. SubgraphX is a similar
-        method that explains\\n\\nGNN predictions by identifying important subgraphs.71\\n\\n
-        \  Another set of approaches like DeepLIFT72 and Layerwise Relevance backPropagation73\\n\\n\\n\\n
-        \                                      8(LRP) are based on backpropagation of
-        the prediction scores through each layer of the neu-\\n\\nral network. The specific
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-        approaches, which means each layer must have its own implementation. Baldas-\\n\\nsarre
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-        that is inspired from the game\\n\\ntheory concept of Shapley values.44,46 SHAP
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-        using Equation 3.79,80\\n\\n\\n\\n                           M\\n                                  1\\n
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-        Equation 3 is an approximation and gives contributions with an expectation\\nterm
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-        science, saliency maps are a way to measure spatial feature contribution.81
-        Simply put,\\n\\nsaliency maps draw a connection between the model\u2019s neural
-        fingerprint components (trained\\n\\nweights) and input features. Weber et al.
-        82 used saliency maps to build an explainable GCN\\n\\narchitecture that gives
-        subgraph importance for small molecule activity prediction. On the\\n\\nother
-        hand, similarity maps compare model predictions for two or more molecules based
-        on\\n\\ntheir chemical fingerprints.83 Similarity maps provide atomic weights
-        or predicte\\n\\n---\\n\\nQuestion: What is XAI?\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        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?\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
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\"}},{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}},{\"type\":\"text\",\"text\":\"Excerpt
-        from wellawatte2023aperspectiveon pages 16-20: 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 55 citations and is from a domain leading peer-reviewed journal.\\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: What is XAI?\"}]}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        an answer. Respond with the following JSON format:\\n\\n{\\n  \\\"summary\\\":
+        \\\"...\\\",\\n  \\\"relevance_score\\\": 0-10\\n}\\n\\nwhere `summary` is relevant
+        information from the text - about 100 words words. `relevance_score` is an integer
+        0-10 for the relevance of `summary` to the question.\\n\\nThe excerpt may or
+        may not contain relevant information. If not, leave `summary` empty, and make
+        `relevance_score` be 0.\"},{\"role\":\"user\",\"content\":\"Excerpt from wellawatte2023aperspectiveon
+        pages 8-9: 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 55 citations and is from a domain
+        leading peer-reviewed journal.\\n\\n---\\n\\nrepresented with equation  2.\\n\\n
+        \                                         \u2206\u02C6f(\u20D7x) \u2248\u2202\u02C6f(\u20D7x)
+        \                                   (2)\\n                               \u2206xi
+        \     \u2202xi\\n\\n\\n\\n                                       7                                                                     \u2206\u02C6f(\u20D7x)
+        \  where \u02C6f(x) is the black-box model and       are used as our attributions.
+        The left-                                                 \u2206xi\\n\\nhand
+        side of equation 2 says that we attribute each input feature xi by how much
+        one unit\\n\\nchange in it would affect the output of \u02C6f(x).  If \u02C6f(x)
+        is a linear surrogate model, then this\\n\\nmethod reconciles with LIME.35 In
+        DL models, \u2207xf(x), suffers from the shattered gradients\\n\\nproblem.62
+        This means directly computing the quantity leads to numeric problems. The\\n\\ndifferent
+        gradient based approaches are mostly distinguishable based on how the gradient
+        is\\n\\napproximated.\\n\\n   Gradient based explanations have been widely used
+        to interpret chemistry predictions.60,66\u201370\\n\\nMcCloskey et al. 60 used
+        graph convolutional networks (GCNs) to predict protein-ligand\\n\\nbinding and
+        explained the binding logic for these predictions using integrated gradients.\\n\\nPope
+        et al. 66 and Jim\xB4enez-Luna et al. 67 show application of gradCAM and integrated
+        gradi-\\n\\nents to explain molecular property predictions from trained graph
+        neural networks (GNNs).\\n\\nSanchez-Lengeling et al. 68 present comprehensive,
+        open-source XAI benchmarks to explain\\n\\nGNNs and other graph based models.
+        They compare the performance of class activation\\n\\nmaps (CAM),63 gradCAM,64
+        smoothGrad,,65 integrated gradients62 and attention mecha-\\n\\nnisms for explaining
+        outcomes of classification as well as regression tasks. They concluded\\n\\nthat
+        CAM and integrated gradients perform well for graph based models. Another attempt\\n\\nat
+        creating XAI benchmarks for graph models was made by Rao et al. 70. They compared\\n\\nthese
+        gradient based methods to find subgraph importance when predicting activity
+        cliffs\\n\\nand concluded that gradCAM and integrated gradients provided the
+        most interpretability\\n\\nfor GNNs.  The GNNExplainer69  is an approach for
+        generating explanations (local and\\n\\nglobal) for graph based models. This
+        method focuses on identifying which sub-graphs con-\\n\\ntribute most to the
+        prediction by maximizing mutual information between the prediction\\n\\nand
+        distribution of all possible sub-graphs. Ying et al. 69 show that GNNExplainer
+        can be\\n\\nused to obtain model-agnostic explanations. SubgraphX is a similar
+        method that explains\\n\\nGNN predictions by identifying important subgraphs.71\\n\\n
+        \  Another set of approaches like DeepLIFT72 and Layerwise Relevance backPropagation73\\n\\n\\n\\n
+        \                                      8(LRP) are based on backpropagation of
+        the prediction scores through each layer of the neu-\\n\\nral network. The specific
+        backpropagation logic across various activation functions differs\\n\\nin these
+        approaches, which means each layer must have its own implementation. Baldas-\\n\\nsarre
+        and Azizpour 74 showed application of LRP to explain aqueous solubility prediction
+        for\\n\\nmolecules.\\n\\n  SHAP is a model-agnostic feature attribution method
+        that is inspired from the game\\n\\ntheory concept of Shapley values.44,46 SHAP
+        has been popularly used in explaining molecular\\n\\nprediction models.75\u201378
+        It\u2019s an additive feature contribution approach, which assumes that\\n\\nan
+        explanation model is a linear combination of binary variables z.  If the Shapley
+        value\\nfor the ith feature is \u03D5i, then the explanation is \u02C6f(\u20D7x)
+        = Pi \u03D5i(\u20D7x)zi(\u20D7x). Shapley values for\\n\\nfeatures are computed
+        using Equation 3.79,80\\n\\n\\n\\n                           M\\n                                  1\\n
+        \                                   \u03D5i(\u20D7x) = X \u02C6f (\u20D7z+i)
+        \u2212\u02C6f (\u20D7z\u2212i)                            (3)\\n                 M\\n\\n
+        \  Here \u20D7z is a fabricated example created from the original \u20D7x and
+        a random perturbation \u20D7x\u2032.\\n\\n\u20D7z+i has the feature i from \u20D7x
+        and \u20D7z\u2212i has the ith feature from \u20D7x\u2032. Some care should
+        be taken\\n\\nin constructing \u20D7z when working with molecular descriptors
+        to ensure that an impossible \u20D7z is\\n\\nnot sampled (e.g., high count of
+        acid groups but no hydrogen bond donors). M is the sample\\n\\nsize of perturbations
+        around \u20D7x. Shapley value computation is expensive, hence M is chosen\\n\\naccordingly.
+        Equation 3 is an approximation and gives contributions with an expectation\\nterm
+        as \u03D50 + Pi=1 \u03D5i(\u20D7x) = \u02C6f(\u20D7x).\\n\\n   Visualization
+        based feature attribution has also been used for molecular data. In com-\\n\\nputer
+        science, saliency maps are a way to measure spatial feature contribution.81
+        Simply put,\\n\\nsaliency maps draw a connection between the model\u2019s neural
+        fingerprint components (trained\\n\\nweights) and input features. Weber et al.
+        82 used saliency maps to build an explainable GCN\\n\\narchitecture that gives
+        subgraph importance for small molecule activity prediction. On the\\n\\nother
+        hand, similarity maps compare model predictions for two or more molecules based
+        on\\n\\ntheir chemical fingerprints.83 Similarity maps provide atomic weights
+        or predicte\\n\\n---\\n\\nQuestion: What is XAI?\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
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0.\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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+        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 55 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
+        negative example molecule to permeate\\n\\nthe BBB. Experimental findings by
+        Fischer et al. 120 show that the BBB permeation of\\n\\nmolecules are governed
+        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
+        and\\n\\nacceptors.  These descriptor explanations are quantitative and interpretable
+        by chemists.\\n\\nFinally, we can use a natural language model to summarize
+        the findings into a written\\n\\nexplanation, as shown in the printed text in
+        Figure 2.\\n\\n\\n\\n\\n\\nFigure 1: Counterfactuals of a molecule which cannot
+        permeate the blood-brain barrier.\\nSimilarity is the Tanimoto similarity of
+        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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-        "user", "content": "Answer the question below with the context.\n\nContext:\n\npqac-91399209:
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         Explainable Artificial Intelligence (XAI) is a field focused on providing interpretations
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+        explanations and descriptor-based explanations are used to provide actionable
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+        These explanations are valuable for understanding and improving model predictions
+        in fields like drug discovery and chemical design.\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.
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+        XAI, or Explainable Artificial Intelligence, refers to methods and techniques
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+        explanations and descriptor explanations. Counterfactual explanations involve
+        minimal changes to a molecule''s structure to alter its predicted properties,
+        while descriptor explanations use surrogate models to attribute predictions
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+        machine learning models for domain experts, aiding in understanding structure-property
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+        19:2149-2160, Mar 2023. URL: https://doi.org/10.1021/acs.jctc.2c01235, doi:10.1021/acs.jctc.2c01235.
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diff --git a/uv.lock b/uv.lock
index fc0b8ee51..3da42ee6b 100644
--- a/uv.lock
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     { name = "transformers" },
+    { name = "tree-sitter" },
+    { name = "tree-sitter-c" },
+    { name = "tree-sitter-java" },
+    { name = "tree-sitter-javascript" },
+    { name = "tree-sitter-python" },
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