ReviewBriefings in bioinformatics2021
Machine learning approaches for drug combination therapies.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
51 citing papers in PubMed, 104 citations in OpenAlex.
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- Reprogramming Autophagy to Strengthen Antitumour Immunity: Advances in Immunotherapeutic Strategies.Immunology · 2026Review
- Deciphering the Mechanisms of Statin-Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data.CPT: pharmacometrics & systems pharmacology · 2026Article
- Clinical innovations and future directions of nanoparticles in the treatment of psychiatric and neurological disorders.Molecular psychiatry · 2026Review
- DDI-HierPred: An Artificial Intelligence-Based Hierarchical PK/PD Platform for Drug-Drug Interaction Prediction.Pharmaceutics · 2026Article
- Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.Journal of cheminformatics · 2026Article
- Nanostructured lipid carriers as co-delivery systems for cancer therapy: Prospects and challenges.International journal of pharmaceutics: X · 2026Review
- p53: from understanding its structure to advances in therapeutic targeting.Signal transduction and targeted therapy · 2026Review
- One-hot news: drug synergy models shortcut molecular features.Bioinformatics (Oxford, England) · 2026Article
- Molecular Compatibility in Chinese Medicine for Cancer Treatment and Drug Development: From Traditional Wisdom to Innovative Therapeutics.Chinese journal of integrative medicine · 2026Article
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- PD-1/PD-L1 Cancer Immunotherapeutics Reshape Tumor Microenvironment - Clinical Evidence and Molecular Mechanisms for AI-based Precision Medicine.Clinical reviews in allergy & immunology · 2025Review
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- CASynergy: A causal attention model for interpretable prediction of cancer drug synergy.PLoS computational biology · 2025Article
- Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells.iScience · 2025Article
- Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.Molecular diversity · 2025Article
- Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 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
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.
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What OpenQuestion holds
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.