Vilya-1: An all-atom foundation model for macrocycle structure prediction and design
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 o...
| Authors | Vilya Research, :, Pascal Sturmfels et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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