Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs

Understand

Pretraining molecular representation models without labels is fundamental to various applications.

• Conventional methods mainly process 2D molecular graphs and focus solely on 2D tasks, making their pretrained models incapable of characterizing 3D geometry and thus defective for downstream 3D tasks.
• In this work, we tackle 3D molecular pretraining in a complete and novel sense.
• In particular, we first propose to adopt an equivariant energy-based model as the backbone for pretraining, which enjoys the merits of fulfilling the symmetry of 3D space.