ArticleNature communications2025
A Bayesian active learning platform for scalable combination drug screens.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Review
- AI-guided precise design of antimicrobial polymers through high-throughput screening technology on an automated platform.Bioactive materials · 2026Article
- [Antibacterial activity and mechanism of the antiparasitic drug resorantel againstZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Artificial Intelligence as a Catalyst for Antimicrobial Discovery: From Predictive Models to De Novo Design.Microorganisms · 2026Review
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- Patient-derived organoids in functional precision oncology: from experimental models to clinical decision-making.Frontiers in endocrinology · 2026Review
- MORPH Predicts the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities.bioRxiv : the preprint server for biology · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Causal machine learning for single-cell genomics.Nature genetics · 2025Review
- A Bayesian active learning platform for scalable combination drug screens.Nature communications · 2025Article
- Spatiotemporal application of small molecules in fracture healing.Biomaterials translational · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. On retrospective experiments from previous large-scale screens, BATCHIE designs rapidly discover highly effective and synergistic combinations. In a prospective combination screen of a library of 206 drugs on a collection of pediatric cancer cell lines, the BATCHIE model accurately predicts unseen combinations and detects synergies after exploring only 4% of the 1.4M possible experiments. Further, the model identifies a panel of top combinations for Ewing sarcomas, which follow-up validation experiments confirm to be effective, including the rational and translatable top hit of PARP plus topoisomerase I inhibition. These results demonstrate that adaptive experiments can enable large-scale unbiased combination drug screens with a relatively small number of experiments. BATCHIE is open source and publicly available ( https://github.com/tansey-lab/batchie ).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.