title: NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
    Generation
summary: 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.
paper_authors._id: 67b6acdb3a3df2f965e7af0b
paper_authors.name: Zhiyuan Liu
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paper_authors.fullname: Zhiyuan Liu
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paper_authors.user: acharkq
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paper_authors.name: Yanchen Luo
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paper_authors.fullname: LuoYanchen
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paper_authors.user: lyc0930
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paper_authors.name: Han Huang
paper_authors._id: 67b6acdb3a3df2f965e7af0e
paper_authors.name: Enzhi Zhang
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paper_authors.fullname: EnzhiZhang
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paper_authors.user: EnzhiZhang
paper_authors._id: 67b6acdb3a3df2f965e7af0f
paper_authors.name: Sihang Li
paper_authors._id: 67b6acdb3a3df2f965e7af10
paper_authors.name: Junfeng Fang
paper_authors._id: 67b6acdb3a3df2f965e7af11
paper_authors.name: Yaorui Shi
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paper_authors.fullname: Yaorui SHI
paper_authors.type: user
paper_authors.user: yrshi
paper_authors._id: 67b6acdb3a3df2f965e7af12
paper_authors.name: Xiang Wang
paper_authors.status: extracted_pending
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paper_authors._id: 65fca775fa59bdf4737b1a84
paper_authors.avatarUrl: /avatars/a161b510bde8f57e7686cbb0b4aa6a52.svg
paper_authors.fullname: Xiang Wang
paper_authors.type: user
paper_authors.user: xiangwang1223
paper_authors._id: 67b6acdb3a3df2f965e7af13
paper_authors.name: Kenji Kawaguchi
paper_authors._id: 67b6acdb3a3df2f965e7af14
paper_authors.name: Tat-Seng Chua
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:42:12.905Z
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paper_authors.avatarUrl: /avatars/21f7d79d46ac4df0ecff8eca7678b33f.svg
paper_authors.fullname: Tat-Seng Chua
paper_authors.type: user
paper_authors.user: chuats
paper_title: NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
    Generation
paper_summary: 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.
paper_ai_keywords: diffusion models 3D conformers SELFIES-based Language Models CVusion model NExT-Mol prompt-based Language Modeling 3D FCD GEOM-DRUGS QM9-2014