Agentic campaign control for high-throughput de novo binder design

Progress in artificial intelligence has produced a rapidly growing ecosystem of powerful methods for de novo protein design. With access to many capable and complementary protein design models, how do we choose and use them effectively, especially under a finite computational budget? Here, we introduce Target-adaptive Rescue–Explore–eXploit (T-REX), an agentic protein design framework that adaptively orchestrates multiple state-of-the-art protein generative models and structure evaluators over the course of a design campaign. Within \trex, large language model (LLM) agents interpret accumulating campaign outcomes to decide whether to rescue promising candidates, explore alternative settings, or exploit productive ones, while a deterministic execution layer validates and schedules proposed actions. We find that LLM agents can integrate structured feedback, including different categories of failure modes, with semantic reasoning to propose new design strategies. Across seven de novo protein-binder design targets, T-REX achieved the highest throughput of structurally distinct hits under matched GPU budgets, while adapting its allocation across models and strategies in a target-dependent manner. T-REX outperformed a non-agentic tree-search controller by 2.43-fold and the strongest individual generator for each target by 2.48-fold on average. These results suggest that as protein-design models become increasingly capable, adaptive orchestration of these models can provide a complementary route to improving design campaigns.