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Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

preprint
submitted on 18.11.2020, 15:30 and posted on 19.11.2020, 12:20 by Jose Jimenez-Luna, Miha Skalic, Nils Weskamp, Gisbert Schneider
Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo 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, cardiac potassium 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.

Funding

SNF 205321_182176

History

Email Address of Submitting Author

jose.jimenez@rethink.ethz.ch

Institution

ETH Zurich

Country

Switzerland

ORCID For Submitting Author

0000-0002-5335-7834

Declaration of Conflict of Interest

G.S. is a cofounder of inSili.com LLC, Zurich, and a consultant to the pharmaceutical industry.

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