GEM-2: Next Generation Molecular Property Prediction Network with Many-body and Full-range Interaction ModelingMolecular property prediction is a fundamental task in the drug and material
industries. Physically, the properties of a molecule are determined by its own
electronic structure, which can be exactly described by the Schrödinger
equation. However, solving the Schrödinger equation for most molecules is
extremely challenging due to long-range interactions in the behavior of a
quantum many-body system. While deep learning methods have proven to be
effective in molecular property prediction, we design a novel method, namely
GEM-2, which comprehensively considers both the long-range and many-body
interactions in molecules. GEM-2 consists of two interacted tracks: an
atom-level track modeling both the local and global correlation between any two
atoms, and a pair-level track modeling the correlation between all atom pairs,
which embed information between any 3 or 4 atoms. Extensive experiments
demonstrated the superiority of GEM-2 over multiple baseline methods in quantum
chemistry and drug discovery tasks.
arxiv.org