Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules
Name
mcdonald-et-al-2025-bayesian-optimization-over-multiple-experimental-fidelities-accelerates-automated-discovery-of-drug.pdf
Description
Published version
Size
7.12 MB
Format
Adobe PDF
Checksum (MD5)
6922952fddc8b46e024778a40d2737d0
Author(s)
McDonald, Matthew A
Koscher, Brent A
Canty, Richard B
Zhang, Jason
Ning, Angelina
Jensen, Klavs F
Date Issued
February 5, 2025
Journal
ACS Central Science
Publisher
American Chemical Society
Citation
Matthew A. McDonald, Brent A. Koscher, Richard B. Canty, Jason Zhang, Angelina Ning, and Klavs F. Jensen
ACS Central Science 2025 11 (2), 346-356.
Version
Final published version
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 costs, and the quality of the data they produce. We first demonstrate the utility of the multifidelity Bayesian optimization (MF-BO) approach on a series of drug targets with data reported in ChEMBL, emphasizing what properties of the chemical search space result in substantial acceleration with MF-BO. Then we integrate the MF-BO experiment selection algorithm into an autonomous molecular discovery platform to illustrate the prospective search for new histone deacetylase inhibitors using docking scores, single-point percent inhibitions, and dose-response IC50 values as low-, medium-, and high-fidelity experiments. A chemical search space with appropriate diversity and fidelity correlation for use with MF-BO was constructed with a genetic generative algorithm. The MF-BO integrated platform then docked more than 3,500 molecules, automatically synthesized and screened more than 120 molecules for percent inhibition, and selected a handful of molecules for manual evaluation at the highest fidelity. Many of the molecules screened have never been reported in any capacity. At the end of the search, several new histone deacetylase inhibitors were found with submicromolar inhibition, free of problematic hydroxamate moieties that constrain the use of current inhibitors.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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DOI of Published Version
https://doi.org/10.1021/acscentsci.4c01991
