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Poster
Deep Molecular Representation Learning via Fusing Physical and Chemical Information
shuwen yang · Ziyao Li · Guojie Song · Lingsheng Cai

Wed Dec 08 12:30 AM -- 02:00 AM (PST) @

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.

Author Information

shuwen yang (Peking University)
Ziyao Li (Peking University)
Guojie Song (Peking University)
Lingsheng Cai (Peking University)

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