ArticleNature communications2020
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.
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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
70 citing papers in PubMed, 1 synthesis or guideline pooled it, 147 citations in OpenAlex.
- Opportunities and challenges of proximity labeling for microbe-host cell interactions in tumor microenvironment.Frontiers in cellular and infection microbiology · 2025Pooled it
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- DeepSynBa: actionable drug combination prediction with complete dose-response profiles.Bioinformatics (Oxford, England) · 2026Article
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- Characterizing clinical toxicity in cancer combination therapies.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
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- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- An open-source screening platform accelerates discovery of drug combinations.Nature communications · 2025Article
- Transfer Learning and Permutation-Invariance Improving Predicting Genome-Wide, Cell-Specific and Directional Interventions Effects of Complex Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- BridgeSyn: a bridging fusion framework for drug combination synergy prediction.Briefings in bioinformatics · 2025Article
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Article
- MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.Bioinformatics (Oxford, England) · 2025Article
- Scaling up drug combination surface prediction.Briefings in bioinformatics · 2025Article
- Predicting drug combination response surfaces.npj drug discovery · 2025Article
- A Bayesian active learning platform for scalable combination drug screens.Nature communications · 2025Article
- Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data.PeerJ · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.
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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.