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+        Verification\\n\\n    and Validation Workshops (ICSTW) 2020, 167\u2013170.\\n\\n\\n(31)
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-        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
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-        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?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        from Wellawatte2023 pages 14-16: Wellawatte et al, XAI Review, 2023\\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:
+        What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
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         1-10 for the relevance of `summary` to the question.\\n\"},{\"role\":\"user\",\"content\":\"Excerpt
-        from Wellawatte2023 pages 14-16: Wellawatte et al, XAI Review, 2023\\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:
-        What is XAI?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
+        from Wellawatte2023 pages 8-9: Wellawatte et al, XAI Review, 2023\\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?\\n\\n\"}],\"model\":\"gpt-4o-2024-11-20\",\"n\":1,\"temperature\":0.0}"
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-        Fulfill Clinical Requirements? Proceedings of\\n\\n     the AAAI Conference
-        on Artificial Intelligence 2022, 36, 11945\u201311953.\\n\\n\\n(38) Zhang, Y.;
-        Xu, F.; Zou, J.; Petrosian, O. L.; Krinkin, K. V. XAI Evaluation: Evalu-\\n\\n
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-        Conference\\n\\n    on Neural Networks and Neurotechnologies (NeuroNT). 2021;
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-        K. T. Interpretable and Explain-\\n\\n     able Machine Learning for Materials
-        Science and Chemistry. Accounts of Materials\\n\\n     Research 2022, 3, 597\u2013607.\\n\\n\\n(40)
-        Yalcin, O.; Fan, X.; Liu, S. Evaluating the correctness of explainable AI algorithms\\n\\n
-        \     for classification. arXiv preprint arXiv:2105.09740 2021,\\n\\n\\n(41)
-        Hoffman, R. R.; Mueller, S. T.; Klein, G.; Litman, J. Metrics for Explainable
-        AI:\\n\\n     Challenges and Prospects. 2018,\\n\\n\\n(42) Mohseni, S.; Zarei,
-        N.; Ragan, E. D. A Multidisciplinary Survey and Framework for\\n\\n     Design
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-        O.; Kim, C.; Bastani, H. Interpreting blackbox models via model extraction.\\n\\n
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-        scheme.\\n\\n     Nanotoxicology 2018, 12, 1\u201317.\\n\\n\\n(51) Han, L.;
-        Wang, Y.; Bryant, S. H. Developing and validating predictive decision tree\\n\\n
-        \    models from mining chemical structural fingerprints and high\u2013throughput
-        screening\\n\\n     data in PubChem. BMC Bioinformatics 2008, 9, 401.\\n\\n(52)
-        Plumb, G.; Al-Shedivat, M.; Cabrera, \xB4A. A.; Perer, A.; Xing, E.; Talwalkar,
-        A. Regu-\\n\\n\\n\\n\\n                                      29      larizing
-        black-box models for improved interpretability. Advances in Neural Informa-\\n\\n
-        \    tion Processing Systems 2020, 33, 10526\u201310536.\\n\\n\\n(53) Shao,
-        X.; Skryagin, A.; Stammer, W.; Schramowski, P.; Kersting, K. Right for bet-\\n\\n
-        \     ter reasons: Training differentiable models by constraining their influence
-        functions.\\n\\n     Proceedings of the AAAI Conference on Artificial Intelligence.
-        2021; pp 9533\u20139540.\\n\\n\\n(54) Ouyang, R.; Curtarolo, S.; Ahmetcik, E.;
-        Scheffler, M.; Ghiringhelli, L. M. SISSO: A\\n\\n     compressed-sensing method
-        for identifying the best low-dimensional descriptor in an\\n\\n     immensity
-        of offered candidates. Physical Review Materials 2018, 2, 083802.\\n\\n\\n(55)
-        Lipton, Z. C. The mythos of model interpretability: In machine learning, the
-        concept\\n\\n      of interpretability is both important and slippery. Queue
-        2018, 16, 31\u201357.\\n\\n\\n(56) Harren, T.; Matter, H.; Hessler, G.; Rarey,
-        M.; Grebner, C. Interpretation of structure\u2013\\n\\n      activity relationships
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+        from Wellawatte2023 pages 28-30: Wellawatte et al, XAI Review, 2023\\n\\n----\\n\\n
+        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
+        on knowledge discovery and data mining. San Diego, CA, USA, 2016; pp\\n\\n     1135\u20131144.\\n\\n\\n(36)
+        Dhurandhar, A.; Chen, P.-Y.; Luss, R.; Tu, C.-C.; Ting, P.; Shanmugam, K.; Das,
+        P.\\n\\n     Explanations based on the missing: Towards contrastive explanations
+        with pertinent\\n\\n     negatives. Advances in neural information processing
+        systems 2018, 31.\\n\\n\\n(37) Jin, W.; Li, X.; Hamarneh, G. Evaluating Explainable
+        AI on a Multi-Modal Medical\\n\\n     Imaging Task: Can Existing Algorithms
+        Fulfill Clinical Requirements? Proceedings of\\n\\n     the AAAI Conference
+        on Artificial Intelligence 2022, 36, 11945\u201311953.\\n\\n\\n(38) Zhang, Y.;
+        Xu, F.; Zou, J.; Petrosian, O. L.; Krinkin, K. V. XAI Evaluation: Evalu-\\n\\n
+        \    ating Black-Box Model Explanations for Prediction. 2021 II International
+        Conference\\n\\n    on Neural Networks and Neurotechnologies (NeuroNT). 2021;
+        pp 13\u201316.\\n\\n\\n(39) Oviedo, F.; Ferres, J. L.; Buonassisi, T.; Butler,
+        K. T. Interpretable and Explain-\\n\\n     able Machine Learning for Materials
+        Science and Chemistry. Accounts of Materials\\n\\n     Research 2022, 3, 597\u2013607.\\n\\n\\n(40)
+        Yalcin, O.; Fan, X.; Liu, S. Evaluating the correctness of explainable AI algorithms\\n\\n
+        \     for classification. arXiv preprint arXiv:2105.09740 2021,\\n\\n\\n(41)
+        Hoffman, R. R.; Mueller, S. T.; Klein, G.; Litman, J. Metrics for Explainable
+        AI:\\n\\n     Challenges and Prospects. 2018,\\n\\n\\n(42) Mohseni, S.; Zarei,
+        N.; Ragan, E. D. A Multidisciplinary Survey and Framework for\\n\\n     Design
+        and Evaluation of Explainable AI Systems. ACM Transactions on Interactive\\n\\n
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+        Montanari, F.; Wolf, T.; Huber, F.; Henderson, R.; Hein-\\n\\n      rich, J.;
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+        \    chemical model explanations. Journal of Cheminformatics 2022, 14, 1\u201314.\\n\\n\\n
+        \                                     28(44) Lundberg, S. M.; Lee, S.-I. In
+        Advances in Neural Information Processing Systems\\n\\n     30; Guyon, I., Luxburg,
+        U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S.,\\n\\n     Garnett,
+        R., Eds.; Curran Associates, Inc., 2017; pp 4765\u20134774.\\n\\n(45) \u02C7Strumbelj,
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+        Santa Monica, CA,\\n\\n     1952.\\n\\n\\n(47) Molnar, C.; Casalicchio, G.;
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+        and challenges. Joint European Conference on Machine Learning and\\n\\n    Knowledge
+        Discovery in Databases. 2020; pp 417\u2013431.\\n\\n\\n(48) Lou, Y.; Caruana,
+        R.; Gehrke, J. Intelligible models for classification and regression.\\n\\n
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+        dis-\\n\\n     covery and data mining. 2012; pp 150\u2013158.\\n\\n\\n(49) Bastani,
+        O.; Kim, C.; Bastani, H. Interpreting blackbox models via model extraction.\\n\\n
+        \    arXiv preprint arXiv:1705.08504 2017,\\n\\n\\n(50) Gajewicz, A.; Puzyn,
+        T.; Odziomek, K.; Urbaszek, P.; Haase, A.; Riebeling, C.;\\n\\n     Luch, A.;
+        Irfan, M. A.; Landsiedel, R.; van der Zande, M.; Bouwmeester, H. Deci-\\n\\n
+        \    sion tree models to classify nanomaterials according to the DF4nanoGrouping
+        scheme.\\n\\n     Nanotoxicology 2018, 12, 1\u201317.\\n\\n\\n(51) Han, L.;
+        Wang, Y.; Bryant, S. H. Developing and validating predictive decision tree\\n\\n
+        \    models from mining chemical structural fingerprints and high\u2013throughput
+        screening\\n\\n     data in PubChem. BMC Bioinformatics 2008, 9, 401.\\n\\n(52)
+        Plumb, G.; Al-Shedivat, M.; Cabrera, \xB4A. A.; Perer, A.; Xing, E.; Talwalkar,
+        A. Regu-\\n\\n\\n\\n\\n                                      29      larizing
+        black-box models for improved interpretability. Advances in Neural Informa-\\n\\n
+        \    tion Processing Systems 2020, 33, 10526\u201310536.\\n\\n\\n(53) Shao,
+        X.; Skryagin, A.; Stammer, W.; Schramowski, P.; Kersting, K. Right for bet-\\n\\n
+        \     ter reasons: Training differentiable models by constraining their influence
+        functions.\\n\\n     Proceedings of the AAAI Conference on Artificial Intelligence.
+        2021; pp 9533\u20139540.\\n\\n\\n(54) Ouyang, R.; Curtarolo, S.; Ahmetcik, E.;
+        Scheffler, M.; Ghiringhelli, L. M. SISSO: A\\n\\n     compressed-sensing method
+        for identifying the best low-dimensional descriptor in an\\n\\n     immensity
+        of offered candidates. Physical Review Materials 2018, 2, 083802.\\n\\n\\n(55)
+        Lipton, Z. C. The mythos of model interpretability: In machine learning, the
+        concept\\n\\n      of interpretability is both important and slippery. Queue
+        2018, 16, 31\u201357.\\n\\n\\n(56) Harren, T.; Matter, H.; Hessler, G.; Rarey,
+        M.; Grebner, C. Interpretation of structure\u2013\\n\\n      activity relationships
+        in real-world drug design data sets using explainable artificial\\n\\n      intelligence.
+        Journal of Chemical Information and Modeling 2022, 62,\\n\\n----\\n\\nQuestion:
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-        as ''black-box'' models due to their lack of transparency. XAI aims to enhance
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-        the internal decision-making process of DL models. XAI is particularly important
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-        et al, XAI Review, 2023\n\nWellawatte2023 pages 22-25: XAI, or Explainable Artificial
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-        ensuring the model learns correct chemical principles. Key challenges in XAI
-        for chemistry include representation of explanations, defining molecular distance
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-        systematic frameworks to evaluate the correctness and applicability of explanations.\nFrom
-        Wellawatte et al, XAI Review, 2023\n\nWellawatte2023 pages 25-28: Explainable
-        Artificial Intelligence (XAI) refers to methods and techniques in artificial
-        intelligence that make the decision-making processes of AI systems transparent,
-        interpretable, and understandable to humans. XAI aims to address challenges
-        such as trust, accountability, and fairness in AI systems by providing explanations
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-        and frameworks for responsible AI. XAI also intersects with regulatory and ethical
-        considerations, such as the European Union''s ''right to explanation'' and the
-        White House''s AI Bill of Rights. It is a growing field with applications in
-        diverse domains like chemistry, healthcare, and energy systems.\nFrom Wellawatte
-        et al, XAI Review, 2023\n\nWellawatte2023 pages 3-5: Explainable Artificial
-        Intelligence (XAI) refers to methods and techniques that provide clarity on
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-        deep learning (DL) models. XAI is a two-step process: first, an accurate but
-        uninterpretable model is developed, and then explanations are added to its predictions.
-        These explanations aim to provide insights into the underlying mechanisms of
-        the model''s predictions. XAI methods can be intrinsic (self-explanatory models)
-        or extrinsic (post-hoc explanations applied after training). Evaluating XAI
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-        fidelity, robustness, and succinctness.\nFrom Wellawatte et al, XAI Review,
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+        (XAI) is a field focused on providing interpretations and explanations for deep
+        learning (DL) model predictions, addressing the ''black-box'' nature of these
+        models. XAI aims to enhance trust and usability by offering insights into why
+        a model made a specific prediction. Key concepts in XAI include interpretability
+        (the degree of human understandability of a model), justifications (quantitative
+        metrics that defend the trustworthiness of predictions), and explanations (descriptions
+        of the reasoning behind predictions). XAI is particularly important in fields
+        like chemistry, where understanding structure-property relationships can guide
+        scientific discovery and regulatory compliance.\nFrom Wellawatte et al, XAI
+        Review, 2023\n\nWellawatte2023 pages 22-25: XAI, or Explainable Artificial Intelligence,
+        refers to methods and techniques used to make the predictions and decisions
+        of AI models understandable to humans. In the context of molecular property
+        prediction, XAI aims to explain how models learn chemical principles, fostering
+        trust and enabling users to assess the model''s correctness. Key challenges
+        in XAI for chemistry include representation of explanations (e.g., text, molecular
+        structures), defining molecular distance for counterfactuals, adapting explanations
+        for different audiences (e.g., chemists, doctors), and evaluating the quality
+        of explanations. XAI is particularly important as AI models transition to regulated
+        industries like healthcare and environmental science.\nFrom Wellawatte et al,
+        XAI Review, 2023\n\nWellawatte2023 pages 25-28: Explainable Artificial Intelligence
+        (XAI) refers to methods and techniques in artificial intelligence that make
+        the decision-making processes of AI systems transparent, interpretable, and
+        understandable to humans. XAI aims to address challenges such as trust, accountability,
+        and fairness in AI systems by providing explanations for their predictions or
+        decisions. Key works in the field include studies on model-agnostic counterfactual
+        explanations, locally faithful surrogate models, and frameworks for responsible
+        AI. XAI also intersects with regulatory efforts, such as the European Union''s
+        ''right to explanation'' and the White House''s AI Bill of Rights, emphasizing
+        the importance of ethical and interpretable AI systems.\nFrom Wellawatte et
+        al, XAI Review, 2023\n\nWellawatte2023 pages 3-5: XAI (Explainable Artificial
+        Intelligence) refers to methods and processes that provide explanations for
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+        models. While DL models are often highly accurate, they are less interpretable.
+        XAI aims to bridge this gap by offering explanations that clarify the internal
+        mechanisms of these models. Explanations can be categorized as global or local,
+        and as intrinsic (part of the model) or extrinsic (post-hoc). A good explanation
+        should ideally be actionable, complete, correct, domain-applicable, faithful,
+        robust, and succinct. XAI is particularly valuable in fields like chemical property
+        prediction, where explanations can align with known physical mechanisms.\nFrom
+        Wellawatte et al, XAI Review, 2023\n\nWellawatte2023 pages 12-14: Explainable
+        Artificial Intelligence (XAI) refers to methods and techniques that make the
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+        25-28, Wellawatte2023 pages 3-5, Wellawatte2023 pages 12-14\n\n----\n\nQuestion:
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diff --git a/uv.lock b/uv.lock
index 2c46056e..1d620c9c 100644
--- a/uv.lock
+++ b/uv.lock
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