ArticleBioinformatics (Oxford, England)2023
MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 44 citations in OpenAlex.
- Momentum contrast-enhanced multimodal representation learning for drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- DeepSynBa: actionable drug combination prediction with complete dose-response profiles.Bioinformatics (Oxford, England) · 2026Article
- DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- One-hot news: drug synergy models shortcut molecular features.Bioinformatics (Oxford, England) · 2026Article
- A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- Hgtsynergy: a transfer learning method for predicting anticancer synergistic drug combinations based on a drug-drug interaction heterogeneous graph.BMC bioinformatics · 2026Article
- MetaComb: a meta-learning framework for drug combination response prediction from cell lines to patients.Frontiers in genetics · 2026Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Building a unified model for drug synergy analysis powered by large language models.Nature communications · 2025Article
- Unsupervised cell line embedding using pairwise drug response correlation.Computational and structural biotechnology journal · 2025Article
- CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.Bioinformatics (Oxford, England) · 2024Article
- Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs.Scientific reports · 2024Article
- DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.Frontiers in genetics · 2024Article
- Computational Advancements in Cancer Combination Therapy Prediction.JCO precision oncology · 2023Article
- Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationCombination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values.
resultsHere, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133 722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. AVAILABILITY AND IMPLEMENTATION: An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY.
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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.