Affiliation:
1. Department of Life Science Informatics and Data Science B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany
2. Lamarr Institute for Machine Learning and Artificial Intelligence Rheinische Friedrich-Wilhelms-Universität Bonn Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany
Abstract
AbstractThe use of black box machine learning models whose decisions cannot be understood limits the acceptance of predictions in interdisciplinary research and camouflages artificial learning characteristics leading to predictions for other than anticipated reasons. Consequently, there is increasing interest in explainable artificial intelligence to rationalize predictions and uncover potential pitfalls. Among others, relevant approaches include feature attribution methods to identify molecular structures determining predictions and counterfactuals (CFs) or contrastive explanations. CFs are defined as variants of test instances with minimal modifications leading to opposing predictions. In medicinal chemistry, CFs have thus far only been little investigated although they are particularly intuitive from a chemical perspective. We introduce a new methodology for the systematic generation of CFs that is centered on well‐defined structural analogues of test compounds. The approach is transparent, computationally straightforward, and shown to provide a wealth of CFs for test sets. The method is made freely available.
Subject
Organic Chemistry,General Pharmacology, Toxicology and Pharmaceutics,Molecular Medicine,Drug Discovery,Biochemistry,Pharmacology
Cited by
1 articles.
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