Force field-inspired molecular representation learning for property prediction

Journal of Cheminformatics

Research

Yuchen He¹  Gao-Peng Ren²  Yi-Jian Yin²  Ke-Jun Wu³ 

[1] State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, 310027, Hangzhou, China;Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, 310027, Hangzhou, China;Institute of Zhejiang University-Quzhou, 324000, Quzhou, China;Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering, Zhejiang University, 310027, Hangzhou, China;Institute of Zhejiang University-Quzhou, 324000, Quzhou, China;School of Chemical and Process Engineering, University of Leeds, LS2 9JT, Leeds, UK;

关键词: Molecular representation learning;    Graph neural networks;    Force field;    Molecular property prediction;    Protein–ligand binding affinity;   

DOI  :  10.1186/s13321-023-00691-2

 received in 2022-12-06, accepted in 2023-01-30,  发布年份 2023

来源: Springer

【 摘 要 】

Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein–ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure.

【 授权许可】

CC BY   
© The Author(s) 2023

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