Vilya-1可精准预测任意化学结构的环肽构象并设计新分子，加速药物研发。
Vilya-1: An all-atom foundation model for macrocycle structure prediction and design
- 基于全原子表示和多源数据训练，统一建模各类环肽构象。
- 在几何精度上超越物理方法与现有深度学习模型，覆盖小分子至大环。
- 支持生成设计，可定制化学、结构与成药性特征，适合药物研发者。
环肽类化合物是日益重要的治疗手段，但现有计算方法在建模其结构与性质方面存在局限，泛化能力差。本文提出Vilya-1，一种深度学习模型，解决环肽设计中的两大核心挑战：在任意化学体系中采样生物相关构象，以及预测关键成药性属性如膜渗透性。Vilya-1采用统一的全原子表示，在涵盖多种拓扑结构和化学类别的异构结构数据集上训练。在包含标准与非标准氨基酸的广泛环肽测试中，其几何精度显著优于基于物理的方法、共折叠网络及深度学习构象生成器，同时保持对小分子至大环的广泛化学覆盖。此外，该模型支持生成应用，可设计具有定制化学、结构与性质特征的新环肽。这些能力使Vilya-1成为下一代环肽药物开发的基础模型。
原文摘要 · Abstract (English)
Macrocyclic peptides are an increasingly important therapeutic modality, but existing computational methods for modeling their structures and properties are limited in scope and do not generalize well across the synthetically accessible chemical space. In this work, we introduce Vilya-1, a deep learning model that addresses two central challenges in macrocycle design: sampling biologically relevant conformations across arbitrary chemistries and predicting key developability properties such as membrane permeability. Vilya-1 operates on a uniform all-atom representation and is trained on heterogeneous structural datasets spanning diverse topologies and chemical classes. Across a broad set of macrocycles composed of canonical and non-canonical residues, Vilya-1 substantially improves geometric accuracy relative to physics-based methods, co-folding networks, and deep-learning conformer generators, while maintaining broad chemical coverage that extends to small molecules. Vilya-1 also supports generative applications, enabling the design of novel macrocycles with tailored chemical, structural, and property profiles. Together, these capabilities establish Vilya-1 as a foundation model for accelerating the development of next-generation macrocycle therapeutics.
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