Cited by 96Computational Drug Discovery MethodsExplainable Artificial Intelligence (XAI)Machine Learning in Materials Science

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

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Jiménez-Luna, José, Skalic, Miha, Weskamp, Nils, Schneider, Gisbert·2021·Journal of Chemical Information and Modeling

Published version of a preprint

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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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
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
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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