RECOVER identifies synergistic drug combinations in vitro through sequential model optimization.
CP: Systems biology
active learning
deep learning
drug combination
drug synergy
in vitro screening
machine learning
oncology
sequential model optimization
Journal
Cell reports methods
ISSN: 2667-2375
Titre abrégé: Cell Rep Methods
Pays: United States
ID NLM: 9918227360606676
Informations de publication
Date de publication:
23 Oct 2023
23 Oct 2023
Historique:
received:
03
11
2022
revised:
30
08
2023
accepted:
06
09
2023
medline:
30
10
2023
pubmed:
6
10
2023
entrez:
5
10
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37797618
pii: S2667-2375(23)00251-5
doi: 10.1016/j.crmeth.2023.100599
pmc: PMC10626197
pii:
doi:
Substances chimiques
Drug Combinations
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
100599Informations de copyright
Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests All authors affiliated with Relation Therapeutics receive equity-based compensation in the company. N.V. holds stock in Glyde Bio, Inc., and Innovac Therapeutics, Inc.
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