Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules.

ABSTRACT: Different experiments of differing fidelities are commonly used in the search for new drug molecules. In classic experimental funnels, libraries of molecules undergo sequential rounds of virtual, coarse, and refined experimental screenings, with each level balanced between the cost of experiments and the number of molecules screened. Bayesian optimization offers an alternative approach, using iterative experiments to locate optimal molecules with fewer experiments than large-scale screening, but without the ability to weigh the costs and benefits of different types of experiments. In this work, we combine the multifidelity approach of the experimental funnel with Bayesian optimization to search for drug molecules iteratively, taking full advantage of different types of experiments, their c

SUBMITTER: McDonald MA 

PROVIDER: S-EPMC11869128 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature