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Yanchen Luo

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding

Chang Wu
Zhiyuan Liu
Wen Shu
Liang Wang
Yanchen Luo
Wenqiang Lei
Yatao Bian
Junfeng Fang

Masked graph modeling (MGM) is a promising approach for molecular representation learning (MRL). However, extending the success of re-mask decoding from 2D to 3D MGM is non-trivial, primarily due to two conflicting challenges: avoiding 2D structure leakage to the decoder, while still providing sufficient 2D context for reconstructing re-masked atoms. To address these challenges, we propose 3D-GSRD: a 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding. The core innovation of 3D-GSRD lies in its Selective Re-mask Decoding (SRD), which re-masks only 3D-relevant information from encoder representations while preserving the 2D graph structures. This SRD is synergistically integrated with a 3D Relational-Transformer (3D-ReTrans) encoder alongside a structure-independent decoder. We analyze that SRD, combined with the structure-independent decoder, enhances the encoder's role in MRL. Extensive experiments show that 3D-GSRD achieves strong downstream performance, setting a new state-of-the-art on 7 out of 8 targets in the widely used MD17 molecular property prediction benchmark. The code is released at https: //github. com/WuChang0124/3D-GSRD.

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ICLR Conference 2025 Conference Paper

NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation

Zhiyuan Liu 0001
Yanchen Luo
Han Huang
Enzhi Zhang
Sihang Li 0002
Junfeng Fang
Yaorui Shi
Xiang Wang 0010

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.

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NeurIPS Conference 2025 Conference Paper

Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling

Yanchen Luo
Zhiyuan Liu
Yi Zhao
Sihang Li
Hengxing Cai
Kenji Kawaguchi
Tat-Seng Chua
Yang Zhang

3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically maintain separate latent spaces for invariant and equivariant modalities, reducing efficiency in both training and sampling. In this work, we propose U nified Variational A uto- E ncoder for 3D Molecular Latent Diffusion Modeling ( UAE-3D ), a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space, while maintaining near-zero reconstruction error. This unified latent space eliminates the complexities of handling multi-modality and equivariance when performing latent diffusion modeling. We demonstrate this by employing the Diffusion Transformer--a general-purpose diffusion model without any molecular inductive bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9 datasets demonstrate that our method significantly establishes new benchmarks in both de novo and conditional 3D molecule generation, achieving leading efficiency and quality. On GEOM-Drugs, it reduces FCD by 72. 6% over the previous best result, while achieving over 70% relative average improvements in geometric fidelity. Our code is released at https: //github. com/lyc0930/UAE-3D/.

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ICLR Conference 2024 Conference Paper

Towards 3D Molecule-Text Interpretation in Language Models

Sihang Li 0002
Zhiyuan Liu 0001
Yanchen Luo
Xiang Wang 0010
Xiangnan He 0001
Kenji Kawaguchi
Tat-Seng Chua
Qi Tian 0001

Language Models (LMs) have greatly influenced diverse domains. However, their inherent limitation in comprehending 3D molecular structures has considerably constrained their potential in the biomolecular domain. To bridge this gap, we focus on 3D molecule-text interpretation, and propose 3D-MoLM: 3D-Molecular Language Modeling. Specifically, 3D-MoLM enables an LM to interpret and analyze 3D molecules by equipping the LM with a 3D molecular encoder. This integration is achieved by a 3D molecule-text projector, bridging the 3D molecular encoder’s representation space and the LM’s input space. Moreover, to enhance 3D MoLM’s ability of cross-modal molecular understanding and instruction following, we meticulously curated a 3D molecule-centric instruction tuning dataset – 3D-MoIT. Through 3D molecule-text alignment and 3D molecule-centric instruction tuning, 3D-MoLM establishes an integration of 3D molecular encoder and LM. It significantly surpasses existing baselines on downstream tasks, including molecule text retrieval, molecule captioning, and more challenging open-text molecular QA tasks, especially focusing on 3D-dependent properties. We will release our codes and datasets at https://github.com/lsh0520/3D-MoLM.

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