ArticleCell reports methods2023
RECOVER identifies synergistic drug combinations in vitro through sequential model optimization.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Active learning-guided mechanistic modeling reveals context-specific regulators of CXCL9 expression in pancreatic cancer cells.Molecular systems biology · 2026Article
- DeepSynBa: actionable drug combination prediction with complete dose-response profiles.Bioinformatics (Oxford, England) · 2026Article
- Mevalonate pathway activation in Ewing sarcoma reveals a 3D-specific synergy between statins and BCL-xL inhibition.Molecular therapy. Oncology · 2026Article
- Combined Treatment of Sodium Butyrate and Bromelain Enhanced Anticancer Effects in Colorectal Cancer Cell Lines: A Promising Therapeutic Approach.International journal of molecular sciences · 2025Article
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
- Causal machine learning for single-cell genomics.Nature genetics · 2025Review
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- A guide for active learning in synergistic drug discovery.Scientific reports · 2025Article
- A Bayesian active learning platform for scalable combination drug screens.Nature communications · 2025Article
- DSA-DeepFM: a dual-stage attention-enhanced DeepFM model for predicting anticancer synergistic drug combinations.Bioinformatics advances · 2025Article
Corrections and comments
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Authors and funding
20 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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