RECOVER identifies synergistic drug combinations in vitro through sequential model optimization.

ABSTRACT: For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line-evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In s

SUBMITTER: Bertin P 

PROVIDER: S-EPMC10626197 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature