MolAnchor method for explaining compound predictions based on substructures
In medicinal chemistry, the impact of machine learning remains limited if predictions are not understood, which often precludes experimental follow-up. Therefore, chemically intuitive approaches that aid in model understanding and interpretation at the molecular level of detail are sought after. Whi...
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Elsevier
2024-12-01
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| Series: | European Journal of Medicinal Chemistry Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277241742400102X |
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| author | Alec Lamens Jürgen Bajorath |
| author_facet | Alec Lamens Jürgen Bajorath |
| author_sort | Alec Lamens |
| collection | DOAJ |
| description | In medicinal chemistry, the impact of machine learning remains limited if predictions are not understood, which often precludes experimental follow-up. Therefore, chemically intuitive approaches that aid in model understanding and interpretation at the molecular level of detail are sought after. While feature attribution methods quantifying feature importance for model decisions are widely used in many areas, they must typically be combined with visualization techniques, if possible, to render the results accessible from a chemical viewpoint. On the other hand, there are approaches such as counterfactuals that yield closely related chemical structures with different prediction outcomes, providing direct access to structural features that critically influence model decisions. Herein, we introduce another approach designed to rationalize chemical predictions based on molecular structure. Therefore, we adapt principles underlying the anchor concept from explainable artificial intelligence (XAI) and alter them for molecular machine learning. The resulting method, termed MolAnchor, systematically identifies substructures in test compounds that determine property predictions, thus ensuring chemical interpretability. The MolAnchor methodology is made freely available to the medicinal chemistry community as a part of our study. |
| format | Article |
| id | doaj-art-e94d5ded8f4b4e7c950f25fb23152576 |
| institution | OA Journals |
| issn | 2772-4174 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Medicinal Chemistry Reports |
| spelling | doaj-art-e94d5ded8f4b4e7c950f25fb231525762025-08-20T02:30:42ZengElsevierEuropean Journal of Medicinal Chemistry Reports2772-41742024-12-011210023010.1016/j.ejmcr.2024.100230MolAnchor method for explaining compound predictions based on substructuresAlec Lamens0Jürgen Bajorath1Department 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, D-53115, Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, GermanyDepartment 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, D-53115, Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115, Bonn, Germany; Corresponding author. 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, D-53115 Bonn, Germany.In medicinal chemistry, the impact of machine learning remains limited if predictions are not understood, which often precludes experimental follow-up. Therefore, chemically intuitive approaches that aid in model understanding and interpretation at the molecular level of detail are sought after. While feature attribution methods quantifying feature importance for model decisions are widely used in many areas, they must typically be combined with visualization techniques, if possible, to render the results accessible from a chemical viewpoint. On the other hand, there are approaches such as counterfactuals that yield closely related chemical structures with different prediction outcomes, providing direct access to structural features that critically influence model decisions. Herein, we introduce another approach designed to rationalize chemical predictions based on molecular structure. Therefore, we adapt principles underlying the anchor concept from explainable artificial intelligence (XAI) and alter them for molecular machine learning. The resulting method, termed MolAnchor, systematically identifies substructures in test compounds that determine property predictions, thus ensuring chemical interpretability. The MolAnchor methodology is made freely available to the medicinal chemistry community as a part of our study.http://www.sciencedirect.com/science/article/pii/S277241742400102XMolecular machine learningModel interpretationExplainable artificial intelligenceAnchorsMolecular fragments |
| spellingShingle | Alec Lamens Jürgen Bajorath MolAnchor method for explaining compound predictions based on substructures European Journal of Medicinal Chemistry Reports Molecular machine learning Model interpretation Explainable artificial intelligence Anchors Molecular fragments |
| title | MolAnchor method for explaining compound predictions based on substructures |
| title_full | MolAnchor method for explaining compound predictions based on substructures |
| title_fullStr | MolAnchor method for explaining compound predictions based on substructures |
| title_full_unstemmed | MolAnchor method for explaining compound predictions based on substructures |
| title_short | MolAnchor method for explaining compound predictions based on substructures |
| title_sort | molanchor method for explaining compound predictions based on substructures |
| topic | Molecular machine learning Model interpretation Explainable artificial intelligence Anchors Molecular fragments |
| url | http://www.sciencedirect.com/science/article/pii/S277241742400102X |
| work_keys_str_mv | AT aleclamens molanchormethodforexplainingcompoundpredictionsbasedonsubstructures AT jurgenbajorath molanchormethodforexplainingcompoundpredictionsbasedonsubstructures |