Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space

• URL: http://arxiv.org/abs/2606.26657v1
• Date: Thu, 25 Jun 2026 06:44:15 GMT
• Title: Target-Aware Bandit Allocation for Scalable Surrogate Optimization in Chemical Space
• Abstract summary: We introduce BOBa, a bandit-guided surrogate optimization framework that eliminates full-library inference.<n>By treating partitions as arms in a multi-armed bandit, BOBa concentrates inference and evaluations on empirically promising partitions.<n>Our findings reveal a tunable tradeoff between screening performance and surrogate inference cost, which supports practical optimization over current libraries.
• Score: 0.7817813851272347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a prominent example. While surrogate-based optimization can increase sample efficiency by reducing the number of expensive evaluations, modern molecular libraries have reached billions to trillions of compounds, making full-library surrogate inference itself a major computational bottleneck. We introduce BOBa, a bandit-guided surrogate optimization framework that eliminates full-library inference by adaptively allocating computation across partitions of the action space. By treating partitions as arms in a multi-armed bandit, BOBa concentrates inference and evaluations on empirically promising partitions while maintaining principled exploration. Experiments on real-world synthesis-on-demand libraries demonstrate that optimism-under-uncertainty bandits, combined with meaningful action space partitioning, are essential for effective allocation of inference and evaluations. Our findings reveal a tunable tradeoff between screening performance and surrogate inference cost, which supports practical optimization over current libraries, and establishes a viable route to ultra-large library virtual screening.

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