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

2026-06-25 · Source: Machine Learning · Field: Science & Research — Artificial Intelligence & Machine Learning, Life Sciences & Biology, Physical Sciences & Chemistry · Depth: Expert, quick

Summary

BOBa, a bandit-guided surrogate optimization framework, addresses the significant computational bottleneck of full-library surrogate inference in massive discrete spaces, such as molecular libraries containing billions to trillions of compounds. It eliminates the need for full-library inference by adaptively allocating computation across partitions of the action space, treating these partitions as arms in a multi-armed bandit. This approach concentrates inference and evaluations on empirically promising partitions while maintaining principled exploration. Experiments on real-world synthesis-on-demand libraries confirm that optimism-under-uncertainty bandits, combined with meaningful action space partitioning, are crucial for effective allocation. The framework reveals a tunable tradeoff between screening performance and surrogate inference cost, supporting practical optimization over current libraries and establishing a viable route to ultra-large library virtual screening.

Key takeaway

For research scientists optimizing candidates in ultra-large discrete spaces, BOBa offers a scalable approach by eliminating full-library surrogate inference. You should consider implementing bandit-guided allocation with meaningful partitioning to manage computational costs and achieve effective screening performance. This is particularly relevant for chemical libraries with billions to trillions of compounds, where traditional methods become infeasible.

Key insights

BOBa uses bandit-guided allocation to optimize surrogate inference in massive chemical spaces, avoiding full-library screening.

Principles

• Optimism-under-uncertainty bandits are essential.
• Meaningful action space partitioning is crucial.
• Screening performance and inference cost have a tunable tradeoff.

Method

BOBa treats partitions of the action space as multi-armed bandit arms, adaptively allocating inference and evaluations to empirically promising partitions while ensuring principled exploration.

In practice

• Apply bandit allocation to large discrete spaces.
• Partition action spaces for computational efficiency.
• Balance screening performance with inference cost.

Topics

• Surrogate Optimization
• Multi-Armed Bandits
• Chemical Space
• Drug Discovery
• Machine Learning
• Scalable Optimization

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.