Direct Molecular Conformation Generation

  • Jinhua Zhu ,
  • Yingce Xia ,
  • Chang Liu ,
  • Lijun Wu ,
  • Shufang Xie ,
  • Yusong Wang ,
  • ,
  • Tao Qin ,
  • Wengang Zhou ,
  • Houqiang Liu ,
  • Haiguang Liu ,
  • Tie-Yan Liu

Transactions on Machine Learning Research |

Molecular conformation generation aims to generate three-dimensional coordinates of all
the atoms in a molecule and is an important task in bioinformatics and pharmacology. Previous methods usually first predict the interatomic distances, the gradients of interatomic
distances or the local structures (e.g., torsion angles) of a molecule, and then reconstruct its
3D conformation. How to directly generate the conformation without the above intermediate values is not fully explored. In this work, we propose a method that directly predicts
the coordinates of atoms: (1) the loss function is invariant to roto-translation of coordinates
and permutation of symmetric atoms; (2) the newly proposed model adaptively aggregates
the bond and atom information and iteratively refines the coordinates of the generated
conformation. Our method achieves the best results on GEOM-QM9 and GEOM-Drugs
datasets. Further analysis shows that our generated conformations have closer properties
(e.g., HOMO-LUMO gap) with the groundtruth conformations. In addition, our method
improves molecular docking by providing better initial conformations. All the results demonstrate the effectiveness of our method and the great potential of the direct approach. The
code is released at https://github.com/DirectMolecularConfGen/DMCG.