RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

• DOI: 10.1016/j.crmeth.2023.100599
• Publication Date: 2023-10-04T14:43:46Z
• Authors: Paul Bertin; Jarrid Rector-Brooks; Deepak Sharma; Thomas Gaudelet; Andrew Anighoro; Torsten Gross; Francisco Martíne...; Eileen L. Tang; M.S. Suraj; Cristian Regep; Jeremy B.R. Hayter; Maksym Korablyov; Nicholas Valiante; Almer van der Sloot; Mike Tyers; Charles E.S. Roberts; Michael M. Bronstein; Luke L. Lairson; Jake P. Taylor-King; Yoshua Bengio
• Keywords: Drug Combinations; Science; Report; Neoplasms; Q; Humans; Computational Biology; Drug Synergism; QD415-436; Biochemistry; TP248.13-248.65; CP: Systems biology; Biotechnology

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 silico benchmarking suggests search queries are ∼5-10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.