ArticleJournal of chemical information and modeling2022
Batched Bayesian Optimization for Drug Design in Noisy Environments.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Applying Deep-Learning-DrivenJournal of medicinal chemistry · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Meta-Learning as a Promising Strategy for Lipid Nanoparticle Optimization and Ionizable Lipid Discovery.Nano letters · 2026Article
- Article
- Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum.Journal of chemical information and modeling · 2025Article
- Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules.ACS central science · 2025Article
- Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods.Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Advancing Rare-Earth (4ACS sustainable chemistry & engineering · 2024Article
- Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.Journal of chemical information and modeling · 2024Article
- Bayesian Optimization in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Review
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4 authors.
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Abstract
The early stages of the drug design process involve identifying compounds with suitable bioactivities via noisy assays. As databases of possible drugs are often very large, assays can only be performed on a subset of the candidates. Selecting which assays to perform is best done within an active learning process, such as batched Bayesian optimization, and aims to reduce the number of assays that must be performed. We compare how noise affects different batched Bayesian optimization techniques and introduce a retest policy to mitigate the effect of noise. Our experiments show that batched Bayesian optimization remains effective, even when large amounts of noise are present, and that the retest policy enables more active compounds to be identified in the same number of experiments.
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