AI 中文总结
研究针对现有大环肽结构和性质建模方法的局限，提出全原子深度学习模型Vilya-1，可跨化学结构采样及预测属性，在异构数据集训练，提高几何精度且覆盖小分子，支持生成应用，为大环治疗药物开发加速。
AI 中文摘要
大环肽是一种日益重要的治疗方式，但现有的计算方法在建模其结构和性质时存在局限性，在合成化学空间中泛化性不佳。本文介绍了Vilya-1，这是一个深度学习模型，解决大环设计中的两个核心挑战：跨任意化学结构采样生物学相关构象以及预测关键的可开发性属性，如膜通透性。Vilya-1基于统一的全原子表示，在跨越不同拓扑和化学类别的异构结构数据集上进行训练。在由标准和非标准残基组成的广泛大环集合中，Vilya-1相对于基于物理的方法、共折叠网络和深度学习构象生成器，显著提高了几何精度，同时保持了对小分子的广泛化学覆盖。Vilya-1还支持生成应用，能够设计具有定制化学、结构和性质特征的新型大环。这些能力使Vilya-1成为加速下一代大环治疗药物开发的基础模型。
英文摘要
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.
Comments21 pages, 14 figures
