Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D
  Diffusion
Weitao Du Jiujiu Chen Xuecang Zhang Zhiming Ma Shengchao Liu
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.