arXiv:2606.26657v1 Announce Type: new 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 eliminat
Rapid advancements in AI and increasing computational demands for drug discovery necessitate more efficient optimization methods, making this research timely.
This development allows for significantly faster and more scalable identification of high-utility candidates in massive chemical spaces, critical for drug discovery and materials science.
The bottleneck of surrogate inference for vast molecular libraries is addressed, enabling researchers to explore billions of compounds with reduced computational cost.
- · Pharmaceutical companies
- · Synthetic biology companies
- · AI/ML drug discovery platforms
- · Chemical researchers
- · Traditional high-throughput screening methods
- · Companies reliant on brute-force computational chemistry
Drug discovery timelines and costs could be substantially reduced, accelerating the development of new therapeutics.
The ability to efficiently explore vast chemical spaces might lead to the discovery of entirely new classes of molecules with unforeseen applications.
This could enable personalized medicine approaches by rapidly design bespoke drug candidates for specific genomic profiles.
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Read at arXiv cs.LG