基于分子表面设计肽类药物，提升靶向精度与结合能力。
Surface-based Molecular Design with Multi-modal Flow Matching
- 通过多模态流匹配学习表面几何与生化特性分布
- 在PepMerge基准上全面超越全原子基线模型
- 适合新药研发、蛋白-蛋白互作靶点探索者
治疗性肽类在靶向以往不可成药结合位点方面展现出潜力，近期深度生成模型实现了针对特定蛋白受体的全原子肽类协同设计。然而，分子表面在蛋白质-蛋白质相互作用中的关键作用尚未被充分探索。为此，我们提出一种名为SurfFlow的通用肽类生成范式，这是一种基于分子表面的新型生成算法，可实现肽类序列、结构与表面特征的全面协同设计。SurfFlow采用多模态条件流匹配（CFM）架构，学习表面几何形状与生化特性的分布，从而提升肽类结合准确性。在PepMerge综合性基准测试中，SurfFlow在所有指标上均持续优于全原子基线模型。这些结果凸显了在从头肽类发现中考虑分子表面的优势，并展示了整合多种蛋白模态对更有效治疗性肽类发现的潜力。
原文摘要 · Abstract (English)
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.
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