ReviewNPJ digital medicine2025
Computational frameworks transform antagonism to synergy in optimizing combination therapies.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Machine learning-guided drug repurposing for EGFR inhibition using scaffold-split validation, docking, and molecular dynamics.Journal of computer-aided molecular design · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- The Gut-Liver Axis in Metabolic Dysfunction-Associated Steatotic Liver Disease: From Mechanistic Insights to Precision Therapeutics.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- Applying biotechnology to overcome cancer drug resistance and improve public health outcomes.Osong public health and research perspectives · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Multi-Omics Advances in Major Depressive Disorder for Molecular Insights, Biomarker Discovery, and Therapeutic Development.Aging and disease · 2025Review
- Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.Cancer biology & medicine · 2025Review
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- Article
- The Impact of Antibiotic Therapy on Intestinal Microbiota: Dysbiosis, Antibiotic Resistance, and Restoration Strategies.Antibiotics (Basel, Switzerland) · 2025Review
- Glucagon-like peptide 1 receptor agonists and cancer risk: advancing precision medicine through mechanistic understanding and clinical evidence.Biomarker research · 2025 · on this mapReview
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While drug combinations are increasingly important in disease treatment, predicting their therapeutic interactions remains challenging. This review systematically analyzes computational methods for predicting drug combination effects through multi-omics data integration. We comprehensively assess key algorithms including DrugComboRanker and AuDNNsynergy, and evaluate integration approaches encompassing kernel regression and graph networks. The review elucidates artificial intelligence applications in predicting drug synergistic and antagonistic effects.
Identifiers
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.