Publication date: Jul 22, 2025

In the loop While the antibody therapeutics space possesses a wealth of sequence data, obtaining functional annotations crucial for drug discovery remains an ongoing gap. VISTA addresses this challenge by generating large-scale training datasets on tens of billions of sequence-target protein-protein interactions in a data loop steered with laboratory feedback. -High quality, large-scale, fit-for-purpose datasets” continues to be the tagline for artificial intelligence (AI) models aiming to achieve scalable drug discovery to novel targets. -We saw a real opportunity to use our underlying technology to solve more generic problems around creating sufficient data to train AI models. ” -While computational protein design and genetic variation separately have been useful for decades, combined they are trillion-fold synergistic, filling each other’s serious gaps,” Church told GEN. VISTA emphasizes high clinical relevance by training the initial model on 320 million sequences from healthy humans with N-of-1 patient safety profiles. Traces of these proteins are only revealed on the cell surface as short peptide fragments in a transient complex with human leukocyte antigens (HLA).

Semantics