Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
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Abstract
molecule generation. However, these models are considered "black-box" and "hard-to-debug". This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand-target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically relevant endpoints.
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“Making more transparent AI/ML models, for instance by highlighting the structural moieties that determine a binding event, has been identified as a key factor to gain trust in AI/ML.272”
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- Authors
- Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany
- Boehringer Ingelheim (Germany) (DE)
- Corresponding author
- Jiménez-Luna, José
- Schneider, Gisbert
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-02-25
- Volume
- 61
- Issue
- 3
- Pages
- 1083-1094
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3131648425
- Cited by
- 96
- References
- 96
- Citations by year
- 2026: 16 · 2025: 18 · 2024: 15 · 2023: 25 · 2022: 18 · 2021: 4
- FWCI
- 8.14
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, other-oa)
- Figshare (submittedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Funders
- Eidgen?ssische Technische Hochschule Z?rich
- Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung (05321_182176)
- Boehringer Ingelheim
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- explainable AI, integrated gradients, graph neural networks, molecular property prediction, plasma protein binding, hERG inhibition, passive permeability, cytochrome P450 inhibition, rational molecular design
- MeSH terms
- Artificial Intelligence, Ligands, Neural Networks, Computer, Drug Discovery
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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