Surface-based Molecular Design with Multi-modal Flow Matching
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918277796593664 |
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| author | Wu, Fang Zhou, Zhengyuan Jin, Shuting Zeng, Xiangxiang Leskovec, Jure Xu, Jinbo |
| author_facet | Wu, Fang Zhou, Zhengyuan Jin, Shuting Zeng, Xiangxiang Leskovec, Jure Xu, Jinbo |
| contents | Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04506 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Surface-based Molecular Design with Multi-modal Flow Matching Wu, Fang Zhou, Zhengyuan Jin, Shuting Zeng, Xiangxiang Leskovec, Jure Xu, Jinbo Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery. |
| title | Surface-based Molecular Design with Multi-modal Flow Matching |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2601.04506 |