ArticleScientific reports2023
SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.International journal of molecular sciences · 2026Article
- Enhancing Drug Response Prediction in Epilepsy with Emerging Multimodal Models: Focus on Clinical, Pharmacologic, and Genomic Factors.CNS drugs · 2026Review
- Emerging advancements and expanding technological scope of education and practices in pharmacy and pharmaceutical sciences.BioImpacts : BI · 2026Review
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
- VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.Scientific reports · 2025Article
- Research Progress on Anti-Aging with Natural Products: From Pathway Modulation to AI-Driven Discovery.Biomolecules · 2025Article
- Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells.iScience · 2025Article
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Determining sex differences in aortic valve myofibroblast responses to drug combinations identified using a digital medicine platform.Science advances · 2025Article
- Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways.Scientific reports · 2025Article
- The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations-a narrative review.Discover oncology · 2025Review
- A guide for active learning in synergistic drug discovery.Scientific reports · 2025Article
- DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.Scientific reports · 2025Article
- A comprehensive review of deep learning-based approaches for drug-drug interaction prediction.Briefings in functional genomics · 2025Review
- DSA-DeepFM: a dual-stage attention-enhanced DeepFM model for predicting anticancer synergistic drug combinations.Bioinformatics advances · 2025Article
- A graph attention-based deep learning network for predicting biotech-small-molecule drug interactions.Bioinformatics advances · 2025Article
- Clinical application and drug resistance mechanism of gemcitabine.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs.
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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.