Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design

Abstract:Generative molecular design typically relies on over-generation and post-hoc filtering. Inference-time steering methods like Feynman-Kac (FK) steering improve efficiency by biasing trajectories toward high-reward candidates, yet they optimize within a single fixed configuration. In de novo nanobody design, upfront commitment to conditioning choices like scaffold identity or target epitope creates a bottleneck. Since yield varies across these conditions, steering unpromising setups wastes compute. We recast generative condition selection as an inference-time resource-allocation problem. We propose two dynamic budget allocators: a joint Global Sequential Monte Carlo (SMC) formulation that steers and allocates compute across configurations concurrently, and a decoupled approach adapting Batched Successive Elimination (BaSE). Empirically, these dynamic allocators increase the yield of designable candidates per GPU-hour nearly 5$\times$ without sacrificing diversity across distinct therapeutic targets and generative configurations.

Machine Learning (cs.LG)

arXiv:2610.13844 [cs.LG]

(or arXiv:2610.13844v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.13844 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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