Nesso-1: Accelerating Open-Source Binding Affinity Predictions

Nesso-1 is an open-source, coarse-grained cofolding model from Valence and Recursion designed to predict protein–ligand binding affinity rapidly for small-molecule drug discovery, where researchers need to identify potent target binders while avoiding off-target effects. The authors describe how conventional computational methods trade speed for physical detail, while newer AI cofolding approaches can improve this balance but remain costly, frequently closed-source, and difficult to evaluate fairly because public benchmarks may contain leakage or reward overfitting. Nesso-1 simplifies cofolding by using token-level structural representations and pairwise network features rather than full heavy-atom generation and multiple-sequence alignment, which the team says makes it more than ten times faster than Boltz-2 while matching or exceeding its accuracy across public and proprietary assays. Reportedly capable of making a prediction in about one second on an H100 GPU for targets up to 800–900 residues, the model could support screening of more than one million compounds daily using 12 GPUs. Evaluations on OpenBind and 25 internal biochemical assays suggest improved performance over Boltz-2 in more realistic out-of-distribution settings, although the authors acknowledge that zero-shot predictions on unfamiliar chemistry remain challenging and that the value of atom-level detail requires further study. Nesso-1 is available with permissive open-source weights and code and is already being used in Recursion drug-discovery programs.

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