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Poster
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Shengchao Liu · weitao du · Zhiming Ma · Hongyu Guo · Jian Tang

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #227
in Poster Session 5 »

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks.

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Author Information

Shengchao Liu (Mila, Université de Montréal)
weitao du (University of science and technology of china)
Zhiming Ma
Hongyu Guo (National Research Council of Canada)
Hongyu Guo

Hongyu GUO is a Senior Researcher Officer at the Digital Technologies Research Center of the National Research Council Canada (NRC). He is also an Adjunct Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa.

Jian Tang (Mila)

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