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arXiv · 2611.03844

Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization

Anisha Parsan·Sebastian Ober·Calvin McCarter·Nick Bhattacharya·Hunter Elliott
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Abstract

Therapeutic antibody development is often framed as comprising two discrete stages: initial discovery of novel binders followed by iterative optimization to refine these hits into therapeutic-grade molecules. Models and methods often reflect these stages, with generative structure models applied to discovery and probabilistic black-box optimization methods applied to refinement. Here we propose to unify these stages by smoothly transitioning a generative discovery model into iterative optimization, allowing the handoff between the structure-based prior and supervision from wet lab data to be guided by the posterior uncertainty of a probabilistic surrogate. We show that this yields improvements in optimization efficiency in the low-N regime, as well as improved diversity amongst the optimized molecules.

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Anisha Parsan, Sebastian Ober, Calvin McCarter, Nick Bhattacharya, Hunter Elliott. 2026-11-02. Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization. https://arxiv.org/abs/2611.03844

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