Nesso-1: Accelerating Open-Source Binding Affinity Predictions
TL;DR
Novo-1, a coarse-grained cofolding framework for binding- affinity prediction, offers more than one order of magnitude speed-up over the leading open-source baseline, Boltz-2, and demonstrates meaningful selectivity, separating the binding affinities of identical compounds between on-targets and related off-targets.
Abstract
In this technical report, we introduce Nesso-1, a coarse-grained cofolding framework for binding- affinity prediction. Nesso-1 requires ∼ 1 second per prediction on a single GPU. This offers more than one order of magnitude speed-up over the leading open-source baseline, Boltz-2, which significantly expands the regions of chemical space that can be explored during high-throughput virtual screening. Importantly, Nesso-1 matches or surpasses the accuracy of Boltz-2 over the same benchmarks adopted in their study—which we show reflect in-distribution scenarios—as well as over more challenging out-of-distribution data encompassing the OpenBind affinity benchmark and 25 internal biochemical assays. Notably, Nesso-1 maintains robust predictive accuracy even on assays with extremely low similarity to the training data. Moreover, we highlight examples where Nesso-1 demonstrates meaningful selectivity, separating the binding affinities of identical compounds between on-targets and related off-targets. Nonetheless, zero-shot generalization to real- world medicinal chemistry remains an inherently challenging task; consequently, we acknowledge specific assays where the model’s performance is limited. We open-source Nesso-1: code and weights are available at https://github.com/recursionpharma/nesso