Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for downstream tasks and, in particular, making it almost impossible for 3D geometry prediction/generation. In this paper, we propose a universal 3D MRL framework, called Uni-Mol, that significantly enlarges the representation ability and application scope of MRL schemes. Uni-Mol contains two pretrained models with the same SE(3) Transformer architecture: a molecular model pretrained by 209M…

• PC
  Peng Cheng Laboratory

  Award: PCL2021A12
• NN
  National Natural Science Foundation of China

  Awards: PCL2021A12, No. U2241212, No. 61972401, No. 61932001, No. 61832017, 61932001, 61832017, U2241212
• RU
  Renmin University of China
• MO
  Ministry of Education, India