ArticleComputational and structural biotechnology journal2020
Deep graph embedding for prioritizing synergistic anticancer drug combinations.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed, 124 citations in OpenAlex.
- 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
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation.International journal of molecular sciences · 2026Review
- A causal inference framework for identifying essential genes to enhance drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- PDTSyn: a parameter-decomposed transformer for domain-generalized cell line-aware drug synergy prediction.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
- A directed weighted network-based method for drug combinations identification using drug-target and inter-target regulation.BMC bioinformatics · 2025Article
- Discovering anticancer drug target combinations via network-informed signaling-based approach.Communications medicine · 2025Article
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures.Interdisciplinary sciences, computational life sciences · 2025Article
- Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature.Cancers · 2025Review
- DSA-DeepFM: a dual-stage attention-enhanced DeepFM model for predicting anticancer synergistic drug combinations.Bioinformatics advances · 2025Article
- Review
- Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery.bioRxiv : the preprint server for biology · 2024Article
- Dual-view jointly learning improves personalized drug synergy prediction.Bioinformatics (Oxford, England) · 2024Article
- Complications and comorbidities associated with antineoplastic chemotherapy: Rethinking drug design and delivery for anticancer therapy.Acta pharmaceutica Sinica. B · 2024Review
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers
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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.