دورية أكاديمية
أعرض في EDS

A Perspective on Explanations of Molecular Prediction Models.

التفاصيل البيبلوغرافية
العنوان: A Perspective on Explanations of Molecular Prediction Models.
المؤلفون: Wellawatte GP; Department of Chemistry, University of Rochester, Rochester, New York 14627, United States., Gandhi HA; Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States., Seshadri A; Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States., White AD; Department of Chemical Engineering, University of Rochester, Rochester, New York 14627, United States.
المصدر: Journal of chemical theory and computation [J Chem Theory Comput] 2023 Apr 25; Vol. 19 (8), pp. 2149-2160. Date of Electronic Publication: 2023 Mar 27.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101232704 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-9626 (Electronic) Linking ISSN: 15499618 NLM ISO Abbreviation: J Chem Theory Comput Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: Washington, D.C. : American Chemical Society, c2005-
مستخلص: Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in "black-box" models. Explainable artificial intelligence (XAI) is a branch of artificial intelligence (AI) which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then, we focus on methods developed by our group and their applications in predicting solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can explain DL predictions while giving insight into structure-property relationships. Finally, we discuss how a two-step process of developing a black-box model and explaining predictions can uncover structure-property relationships.
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معلومات مُعتمدة: R35 GM137966 United States GM NIGMS NIH HHS
تواريخ الأحداث: Date Created: 20230327 Date Completed: 20230425 Latest Revision: 20230515
رمز التحديث: 20230515
مُعرف محوري في PubMed: PMC10134429
DOI: 10.1021/acs.jctc.2c01235
PMID: 36972469
قاعدة البيانات: MEDLINE
الوصف
تدمد:1549-9626
DOI:10.1021/acs.jctc.2c01235