ArticleScientific reports2019
In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
- MGCLSyn: multimodal graph contrastive learning for drug synergy prediction.Journal of computer-aided molecular design · 2026Article
- A Machine Learning Approach for Predicting Synergism of Mixtures of Compounds in Treating Cancers.International journal of molecular sciences · 2026Article
- GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Multi agent large language models for biomedical hypothesis generation in drug combination discovery.iScience · 2025Article
- BridgeSyn: a bridging fusion framework for drug combination synergy prediction.Briefings in bioinformatics · 2025Article
- Few-shot drug synergy prediction via rapid cross-tier adaptation meta-optimization.Briefings in bioinformatics · 2025Article
- 1-Nitro-2-Phenylethane as a Multitarget Candidate for Cognitive and Psychiatric Disorders: Insights from In Silico and Behavioral Approaches.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Gene context drift identifies drug targets to mitigate cancer treatment resistance.Cancer cell · 2025Article
- HIG-Syn: a hypergraph and interaction-aware multigranularity network for predicting synergistic drug combinations.Bioinformatics (Oxford, England) · 2025Article
- MADSP: predicting anti-cancer drug synergy through multi-source integration and attention-based representation learning.Bioinformatics (Oxford, England) · 2025Article
- Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.Scientific reports · 2025Article
- AI-driven discovery of synergistic drug combinations against pancreatic cancer.Nature communications · 2025Article
- Computational frameworks transform antagonism to synergy in optimizing combination therapies.NPJ digital medicine · 2025Review
- SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations.GigaScience · 2025Article
- Optimizing drug synergy prediction through categorical embeddings in deep neural networks.Biology methods & protocols · 2025Article
- MD-Syn: synergistic drug combination prediction based on a multidimensional feature fusion method and attention mechanisms.Frontiers in pharmacology · 2025Article
- Prediction of cancer cell line-specific synergistic drug combinations based on multi-omics data.PeerJ · 2025Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model's predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.
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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 OpenQuestion graph.