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Molecular representation learning is the first yet vital step in combiningdeep learning and molecular science. To push the boundaries of molecularrepresentation learning, we present PhysChem, a novel neural architecture thatlearns molecular representations via fusing physical and chemical informationof molecules. PhysChem is composed of a physicist network (PhysNet) and achemist network (ChemNet). PhysNet is a neural physical engine that learnsmolecular conformations through simulating molecular dynamics withparameterized forces; ChemNet implements geometry-aware deep message-passing tolearn chemical / biomedical properties of molecules. Two networks specialize intheir own tasks and cooperate by providing expertise to each other. By fusingphysical and chemical information, PhysChem achieved state-of-the-artperformances on MoleculeNet, a standard molecular machine learning benchmark.The effectiveness of PhysChem was further corroborated on cutting-edge datasetsof SARS-CoV-2.

Deep Molecular Representation Learning via Fusing Physical and Chemical Information