Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
Latent Labs Team: Alex Bridgland, Jonathan Crabb\'e, Henry Kenlay, Daniella Pretorius, Sebastian M. Schmon, Agrin Hilmkil, Rebecca Bartke-Croughan, Robin Rombach, Michael Flashman, Tomas Matteson, Simon Mathis, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl

TL;DR
Latent-X is a novel all-atom protein design model that efficiently generates high-affinity, specific protein binders directly from target epitopes, significantly advancing de novo biologics discovery.
Contribution
It introduces Latent-X, a pioneering atom-level model that jointly generates protein structures and sequences for target binding, outperforming existing methods in speed and efficacy.
Findings
Achieves over 90% hit rate for macrocyclic peptides.
Produces potent binders with low nanomolar affinities.
Generates structurally diverse binders, including complex folds.
Abstract
Traditional drug discovery relies on rounds of screening millions of candidate molecules with low success rates, making drug discovery time and resource intensive. To overcome this screening bottleneck, we introduce Latent-X, an all-atom protein design model that enables a new paradigm of precision AI design. Given a target protein epitope, Latent-X jointly generates the all atom structure and sequence of the protein binder and target, directly modelling the non-covalent interactions essential for specific binding. We demonstrate its efficacy across two therapeutically relevant modalities through extensive wet lab experiments, testing as few as 30-100 designs per target. For macrocyclic peptides, Latent-X achieves experimental hit rates exceeding 90% on all evaluated benchmark targets. For mini-binders, it consistently produces potent candidates against all evaluated benchmark targets,…
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