Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design

• URL: http://arxiv.org/abs/2608.20755v1
• Date: Fri, 21 Aug 2026 05:32:26 GMT
• Title: Natural-Language-Guided Generator-Agnostic Shortlisting for Protein Binder Design
• Abstract summary: Modern de novo design generate many candidate protein binders, but wet-lab validation capacity remains limited.<n>We study whether LLMs can generate multi-metric ranking policies from precomputed structural-confidence and interface-quality proxy scores.
• Score: 16.443448156662644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern de novo design workflows generate many candidate protein binders, but wet-lab validation capacity remains limited, making shortlisting a major bottleneck. We study whether LLMs can generate multi-metric ranking policies from precomputed structural-confidence and interface-quality proxy scores. Rather than proposing a new protein binder design pipeline, we focus on post-generation binder shortlisting: selecting the final top-K candidates from already generated binder pools using a shared panel of precomputed proxy scores. On the 10-target held-out split, averaging performance over five sampled global iterative gpt-4o policies reaches 0.589 Recall@10, modestly improving over the strongest single-feature fixed baseline, Protenix binder ipTM, which reaches 0.571 Recall@10. On the 3-target held-out subset comprising Nipah, RBX1, and TREM2, target-conditioned iterative gpt-5.4 policies reach the strongest LLM performance, with 0.519 Recall@10 and 0.583 NDCG@10. These results suggest that LLM-generated ranking policies can act as an interpretable post-generation decision layer for combining heterogeneous proxy metrics to prioritize binders from large candidate pools.

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