Deep Molecular Representation Learning via Fusing Physical and Chemical Information

Shuwen Yang, Ziyao Li, Guojie Song, Lingsheng Cai

Keywords: molecular representation, molecular fingerprints, neural molecular dynamics, graph neural networks

TL;DR: We present a deep molecular representation method that captures and fuses physical and chemical information of molecules.

Abstract: Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules. PhysChem is composed of a physicist network (PhysNet) and a chemist network (ChemNet). PhysNet is a neural physical engine that learns molecular conformations through simulating molecular dynamics with parameterized forces; ChemNet implements geometry-aware deep message-passing to learn chemical / biomedical properties of molecules. Two networks specialize in their own tasks and cooperate by providing expertise to each other. By fusing physical and chemical information, PhysChem achieved state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. The effectiveness of PhysChem was further corroborated on cutting-edge datasets of SARS-CoV-2.

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Code: https://github.com/PKUterran/PhysChem