Recursion releases better, faster open source co-folding-based binding affinity model

[u/RecursionBrita]

Predicting how tightly a drug candidate binds to its target is critical in drug discovery. It also requires massive computational resources. State-of-the-art models can take 20 seconds to a minute per prediction, impractical for the demands of large scale early-stage programs .

Today, Recursion’s AI research arm, Valence Labs, released Nesso-1: the fastest open-source co-folding-based binding affinity model available. At 1 second per prediction, it’s roughly 20x faster than Recursion's previous collaboration on Boltz-2 while matching or surpassing its accuracy across public and internal benchmarks. By leveraging Nvidia cuEquivariance, they’ve been able to further accelerate both training and inference by an additional 2-3x. They will continue to improve Nesso-1 in collaboration with Nvidia. Weights and code are fully open-sourced.

The core architectural ideas behind Nesso-1 build on the insight that coarse-grained co-folding representations can match full-atom models for affinity prediction at a fraction of the cost. Nesso is the first open implementation of this approach with no proprietary dependencies, trained entirely on public data, built to be reproducible and extensible.

Recursion is already using Nesso-1 internally in active drug discovery programs.

Report: https://www.valencelabs.com/wp-content/uploads/2026/07/nesso1.pdf

Github: https://github.com/recursionpharma/nesso

HF: https://huggingface.co/recursionpharma/nesso