使用Vilya-2对化学性质多样的分子界面进行精确结构建模
Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2
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中文总结 AI 辅助
研究针对肽疗法结构预测难题，提出Vilya-2扩散变换器，能对不同分子与靶点相互作用建模。通过生成多样结构集合排序，在肽界面恢复等方面远超现有模型，还在小分子对接等表现出色，可作基础模型，统一了预测准确性与通用性。
中文摘要 AI 辅助
基于协同进化统计的结构预测网络改变了基于蛋白质的药物发现，但它们的准确性不适用于肽疗法，肽疗法由非经典残基、大环化和复杂拓扑结构定义，是一种越来越重要的药物形式。我们引入了Vilya-2，这是一种扩散变换器，它将Vilya-1的全原子表示从对单个分子建模扩展到对其与蛋白质靶点的相互作用进行建模。这种全原子表示能够在不同分子类型之间进行迁移学习，并能对与治疗相关靶点结合的各种大小、类别和组成的肽进行高精度的结构建模。通过生成不同的结构集合并以校准的置信度对它们进行排序，Vilya-2能够将59.1%的肽界面恢复到主链RMSD小于2 Å，远远超过了代表性的共折叠模型的性能。此外，Vilya-2在小分子对接方面处于领先地位，并且能够推广到与训练中所见不同的新型蛋白质-小分子复合物。它还能推广到对比训练中所见任何分子大几倍的各种大环和二硫键固定的微型蛋白质的分子构象进行建模。最后，Vilya-2可以用作基础模型，并在从命中到先导的活动中进行微调以富集活性化合物。通过将预测准确性与化学空间中的广泛通用性统一起来，Vilya-2是从头肽设计管道所需的结构预测预言机，将全原子方法确立为从头肽疗法设计和评估的一般基础。
英文摘要
Structure-prediction networks built on co-evolutionary statistics have transformed protein-based drug discovery, yet their accuracy does not extend to peptide therapeutics--an increasingly important modality defined by non-canonical residues, macrocyclization, and complex topologies. We introduce Vilya-2, a diffusion transformer that extends the all-atom representation of Vilya-1 from modeling individual molecules to modeling their interactions with protein targets. This all-atom representation enables transfer learning between different molecular types, and delivers highly accurate structural modeling of peptides across sizes, classes, and compositions bound to therapeutically relevant targets. By generating diverse structural ensembles and ranking them with calibrated confidence, Vilya-2 recovers 59.1% of peptide interfaces to sub-2 Å backbone RMSD, far exceeding the performance of a representative co-folding model even when that model is given the bound receptor as a template. In addition, Vilya-2 is state-of-the-art at small-molecule docking, and generalizes to novel protein-small molecule complexes unlike those seen in training. It also generalizes to modeling molecular conformations of diverse macrocycles and disulfide-stapled miniproteins several-fold larger than any molecule seen in training. Finally, Vilya-2 can be used as a foundation model, and fine-tuned to enrich for active compounds in hit-to-lead campaigns. By unifying predictive accuracy with broad generalizability across chemical space, Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
发表机构
- Vilya Research（Vilya研究团队）
机构由 AI 辅助整理，请以论文原文为准。
