Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking

Zhenxing Wu, Jike Wang, Hongyan Du, Dejun Jiang, Yu Kang, Dan Li, Peichen Pan, Yafeng Deng, Dongsheng Cao (), Chang-Yu Hsieh () and Tingjun Hou ()
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Zhenxing Wu: Zhejiang University
Jike Wang: Zhejiang University
Hongyan Du: Zhejiang University
Dejun Jiang: Zhejiang University
Yu Kang: Zhejiang University
Dan Li: Zhejiang University
Peichen Pan: Zhejiang University
Yafeng Deng: CarbonSilicon AI Technology Co., Ltd
Dongsheng Cao: Central South University
Chang-Yu Hsieh: Zhejiang University
Tingjun Hou: Zhejiang University

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood–brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.

Date: 2023
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DOI: 10.1038/s41467-023-38192-3

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