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.
@article{arxiv.2112.04624,
title = {Deep Molecular Representation Learning via Fusing Physical and Chemical Information},
author = {Shuwen Yang and Ziyao Li and Guojie Song and Lingsheng Cai},
journal= {arXiv preprint arXiv:2112.04624},
year = {2021}
}
Comments
In NeurIPS-2021, 18 pages, 5 figures, appendix included