NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
  Generation
Authors: name: Zhiyuan Liu, name: Yanchen Luo, name: Han Huang, name: Enzhi Zhang, name: Sihang Li, name: Junfeng Fang, name: Yaorui Shi, name: Xiang Wang, name: Kenji Kawaguchi, name: Tat-Seng Chua

3D molecule generation is crucial for drug discovery and material design.
While prior efforts focus on 3D diffusion models for their benefits in modeling
continuous 3D conformers, they overlook the advantages of 1D SELFIES-based
Language Models (LMs), which can generate 100% valid molecules and leverage the
billion-scale 1D molecule datasets. To combine these advantages for 3D molecule
generation, we propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D
Language Modeling for 3D Molecule Generation. NExT-Mol uses an extensively
pretrained molecule LM for 1D molecule generation, and subsequently predicts
the generated molecule's 3D conformers with a 3D diffusion model. We enhance
NExT-Mol's performance by scaling up the LM's model size, refining the
diffusion neural architecture, and applying 1D to 3D transfer learning.
Notably, our 1D molecule LM significantly outperforms baselines in
distributional similarity while ensuring validity, and our 3D diffusion model
achieves leading performances in conformer prediction. Given these improvements
in 1D and 3D modeling, NExT-Mol achieves a 26% relative improvement in 3D FCD
for de novo 3D generation on GEOM-DRUGS, and a 13% average relative gain for
conditional 3D generation on QM9-2014. Our codes and pretrained checkpoints are
available at https://github.com/acharkq/NExT-Mol.