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

RECOVER identifies synergistic drug combinations in vitro through sequential model optimization

  • Cell Rep Methods. 2023 Oct 23;3(10):100599. doi: 10.1016/j.crmeth.2023.100599.
Paul Bertin 1,  Jarrid Rector-Brooks 1,  Deepak Sharma 1,  Thomas Gaudelet 2,  Andrew Anighoro 2,  Torsten Gross 2,  Francisco Martínez-Peña 3,  Eileen L Tang 3,  M S Suraj 2,  Cristian Regep 2,  Jeremy B R Hayter 2,  Maksym Korablyov 1,  Nicholas Valiante 4,  Almer van der Sloot 5,  Mike Tyers 6,  Charles E S Roberts 2,  Michael M Bronstein 7,  Luke L Lairson 3,  Jake P Taylor-King 8,  Yoshua Bengio 1
Affiliations

Affiliations

  • 1 Mila, the Quebec AI Institute, Montreal, QC, Canada.
  • 2 Relation Therapeutics, London, UK.
  • 3 Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA.
  • 4 Innovac Therapeutics, Inc., Cambridge, MA, USA.
  • 5 IRIC, Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, QC, Canada.
  • 6 Program in Molecular Medicine, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada.
  • 7 Relation Therapeutics, London, UK; Department of Computer Science, University of Oxford, Oxford, UK.
  • 8 Relation Therapeutics, London, UK. Electronic address: jake@relationrx.com.
PMID: 37797618 DOI: 10.1016/j.crmeth.2023.100599
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 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.

Keywords

CP: Systems biology; active learning; deep learning; drug combination; drug synergy; in vitro screening; machine learning; oncology; sequential model optimization.

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