ArticlePLoS computational biology2011
Prediction of drug combinations by integrating molecular and pharmacological data.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers.
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Who cites it
105 citing papers in PubMed.
- Multi agent large language models for biomedical hypothesis generation in drug combination discovery.iScience · 2025Article
- Accurate prediction of synergistic drug combination using a multi-source information fusion framework.BMC biology · 2025Article
- HLN-DDI: hierarchical molecular representation learning with co-attention mechanism for drug-drug interaction prediction.BMC bioinformatics · 2025Article
- HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm.PloS one · 2025Article
- Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data.PeerJ · 2025Article
- Simulate Scientific Reasoning with Multiple Large Language Models: An Application to Alzheimer's Disease Combinatorial Therapy.medRxiv : the preprint server for health sciences · 2024Article
- Exploring potential therapeutic combinations for castration-sensitive prostate cancer using supercomputers: a proof of concept study.Scientific reports · 2024Article
- A network-based trans-omics approach for predicting synergistic drug combinations.Communications medicine · 2024Article
- MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.BMC bioinformatics · 2024Article
- Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.Acta parasitologica · 2024Article
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.Frontiers in medicine · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- A hybrid deep forest-based method for predicting synergistic drug combinations.Cell reports methods · 2023Article
- CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.Briefings in bioinformatics · 2023Article
- Discovery and Validation of Traditional Chinese and Western Medicine Combination Antirheumatoid Arthritis Drugs Based on Machine Learning (Random Forest Model).BioMed research international · 2023Article
- NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.International journal of molecular sciences · 2022Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Article
- Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes.Journal of cheminformatics · 2022Article
45 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.
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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.