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arXiv Machine Learning By Mason Minot, Simon Friedensohn, C\'edric R. Weber

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

Read the original on arXiv Machine Learning →

The paper introduces dynamic inference-time steering methods for de novo nanobody design, addressing the inefficiency of fixed-condition generative models. Two new budget allocation strategies are proposed: a joint Global Sequential Monte Carlo (SMC) approach that steers and allocates compute across multiple scaffold and epitope configurations simultaneously, and a decoupled Batched Successive Elimination (BaSE) method that adapts resource allocation during inference. Experiments show that these dynamic allocators boost the yield of designable candidates per GPU-hour by nearly fivefold while maintaining diversity across therapeutic targets and generative settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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