ArticleScientific reports2024
Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 96 citations in OpenAlex.
- Protein Spatial Structure Meets Artificial Intelligence: Revolutionizing Drug Synergy-Antagonism in Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Pooled it
- Bioactivity-driven prediction of antibacterial synergy using machine learning models.PLoS computational biology · 2026Article
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- Beyond the barrier: Engineering the tumor-immune-soil nexus-a mechanistic blueprint for integrating Traditional Chinese Medicine with immunotherapy in metastatic colorectal cancer.Medical oncology (Northwood, London, England) · 2026Review
- Overcoming mTOR Inhibitor Resistance: From Biological Basis to Therapeutic Strategies.Biomedicines · 2026Review
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- Crafting an innovative one health-aligned machine learning framework for neglected tropical diseases elimination.Journal of advanced research · 2026Article
- BRAF inhibitor resistance in melanoma: from resistance mechanisms to therapeutic innovations.Molecular biomedicine · 2026Review
- Modeling Microbiome Modulation of Tumor Metabolic Networks to Predict Synergistic Therapies.bioRxiv : the preprint server for biology · 2026Article
- Inflammatory milieu and role of epigenetic modifications in high-grade serous ovarian cancer.Clinical epigenetics · 2026Review
- Characterizing clinical toxicity in cancer combination therapies.Bioinformatics (Oxford, England) · 2026Article
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Utilizing artificial intelligence to assess academic exam anxiety, perceived stress, and achievement motivation among college students.Frontiers in psychiatry · 2026Article
- Transcription co-inhibition alters drug resistance evolution and enhances Mycobacterium tuberculosis clearance from granulomas.Nature microbiology · 2026Article
- The role for artificial intelligence in identifying combination therapies for Alzheimer's disease.The journal of prevention of Alzheimer's disease · 2025Review
- Article
- Integrative profiling strategies to guide personalized therapy in mantle cell lymphoma: a pilot study.NPJ precision oncology · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Optimizing YOLOv11 for automated classification of breast cancer in medical images.Scientific reports · 2025Article
- MolAI: A Deep Learning Framework for Data-Driven Molecular Descriptor Generation and Advanced Drug Discovery Applications.Journal of chemical information and modeling · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves administering two or more anti-cancer agents to increase efficacy and overcome multidrug resistance compared to monotherapy. However, drug combinations can exhibit synergy, additivity, or antagonism. This study presents a machine learning framework to classify and predict cancer drug combinations. The framework utilizes several key steps including data collection and annotation from the O'Neil drug interaction dataset, data preprocessing, stratified splitting into training and test sets, construction and evaluation of classification models to categorize combinations as synergistic, additive, or antagonistic, application of regression models to predict combination sensitivity scores for enhanced predictions compared to prior work, and the last step is examination of drug features and mechanisms of action to understand synergy behaviors for optimal combinations. The models identified combination pairs most likely to synergize against different cancers. Kinase inhibitors combined with mTOR inhibitors, DNA damage-inducing drugs or HDAC inhibitors showed benefit, particularly for ovarian, melanoma, prostate, lung and colorectal carcinomas. Analysis highlighted Gemcitabine, MK-8776 and AZD1775 as frequently synergizing across cancer types. This machine learning framework provides a valuable approach to uncover more effective multi-drug regimens.
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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 Socratic graph.