← 研踪 PaperTrailarXiv:2611.03844 · v1
Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization
首次提交 2026-11-03 03:15
版本更新 2026-11-03 03:15
雷达观测 2026-11-04 23:41
摘要
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.
作者备注：Accepted to NeurIPS `26 GEM and NeurIPS `26 AI4DD
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v1 · 2026-11-03 03:15 · 最新观测版本
Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization
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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