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+        date accessed, the current year is 2024\n\nA Perspective on Explanations of
+        Molecular\nPrediction Models\nGeemi P. Wellawatte,\u2020\nHeta A. Gandhi,\u2021\nAditi
+        Seshadri,\u2021 and\nAndrew\nD. White\u2217,\u2021\n\u2020Department of Chemistry,
+        University of Rochester, Rochester, NY, 14627\n\u2021Department of Chemical
+        Engineering, University of Rochester, Rochester, NY, 14627\n\u00b6Vial Health
+        Technology, Inc., San Francisco, CA 94111\nE-mail: andrew.white@rochester.edu\nAbstract\nChemists
+        can be skeptical in using deep learning (DL) in decision making, due to\nthe
+        lack of interpretability in \u201cblack-box\u201d models. Explainable artificial
+        intelligence\n(XAI) is a branch of AI which addresses this drawback by providing
+        tools to interpret\nDL models and their predictions. We review the principles
+        of XAI in the domain of\nchemistry and emerging methods for creating and evaluating
+        explanations. Then we\nfocus on methods developed by our group and their applications
+        in predicting solubil-\nity, blood-brain barrier permeability, and the scent
+        of molecules. We show that XAI\nmethods like chemical counterfactuals and descriptor
+        explanations can explain DL pre-\ndictions while giving insight into structure-property
+        relationships. Finally, we discuss\nhow a two-step process of developing a black-box
+        model and explaining predictions can\nuncover structure-property relationships.\n1\nIntroduction\nDeep
+        learning (DL) is advancing the boundaries of computational chemistry because
+        it can\naccurately model non-linear structure-function relationships.1\u20133
+        Applications of DL can be\nfound in a broad spectrum spanning from quantum computing4,5
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