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Poster
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
weitao Du · Jiujiu Chen · Xuecang Zhang · Zhi-Ming Ma · Shengchao Liu

Thu Dec 14 03:00 PM -- 05:00 PM (PST) @ Great Hall & Hall B1+B2 #1926
in Poster Session 6 »

Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery. In this work, we propose a pretraining method for molecule joint auto-encoding (MoleculeJAE). MoleculeJAE can learn both the 2D bond (topology) and 3D conformation (geometry) information, and a diffusion process model is applied to mimic the augmented trajectories of such two modalities, based on which, MoleculeJAE will learn the inherent chemical structure in a self-supervised manner. Thus, the pretrained geometrical representation in MoleculeJAE is expected to benefit downstream geometry-related tasks. Empirically, MoleculeJAE proves its effectiveness by reaching state-of-the-art performance on 15 out of 20 tasks by comparing it with 12 competitive baselines.

Author Information

weitao Du (University of Science and Technology of China)
Jiujiu Chen (University of Electronic Science and Technology of China)
Xuecang Zhang (Huawei Technologies)
Zhi-Ming Ma
Shengchao Liu (UC Berkeley & Caltech)

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