Vilya-1 Foundation Model Predicts and Designs Macrocycle Structures.

Researchers have developed Vilya-1, an all-atom deep learning foundation model aimed at revolutionizing macrocycle structure prediction and design. Macrocyclic peptides are increasingly vital in therapeutics, but current computational methods struggle with generalization across varied chemical spaces. Vilya-1 addresses this by uniformly representing all atoms and training on heterogeneous structural datasets, encompassing diverse topologies and chemical classes. The model excels at sampling biologically relevant conformations and predicting crucial developability properties, such as membrane permeability. Vilya-1 demonstrates substantial improvements in geometric accuracy compared to physics-based methods, co-folding networks, and other deep-learning conformer generators. Beyond prediction, it supports generative applications, allowing for the design of new macrocycles with specific chemical, structural, and property profiles. This establishes Vilya-1 as a foundational tool to accelerate the development of next-generation macrocycle therapeutics.