Biology subjects

Bengio, E.

Publications and source records attributed to Bengio, E..

2 recordsLinked to original sources

bioRxiv2026

Nesso-1: Accelerating Open-Source Binding Affinity Predictions

In this technical report, we introduce NO_SCPLOWESSOC_SCPLOW-1, a coarse-grained cofolding framework for binding- affinity prediction. NO_SCPLOWESSOC_SCPLOW-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, NO_SCPLOWESSOC_SCPLOW-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, NO_SCPLOWESSOC_SCPLOW-1 maintains robust predictive accuracy even on assays with extremely low similarity to the training data. Moreover, we highlight examples where NO_SCPLOWESSOC_SCPLOW-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 models performance is limited. We open-source NO_SCPLOWESSOC_SCPLOW-1: code and weights are available at https://github.com/recursionpharma/nesso

molecular biology↗

bioRxiv2018

Fooling the classifier: Ligand antagonism and adversarial examples

Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.

biophysics↗