ArticleJournal of cheminformatics2023
DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A Hypergraph-Based Model for Predicting Potential Drug Combinations in Cancer Therapy.Interdisciplinary sciences, computational life sciences · 2026Article
- ComplexMatrixComb: predicting anticancer drug combination dosages for target growth inhibition via complex-number matrix factorization.Journal of cheminformatics · 2026Article
- GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- A causal inference framework for identifying essential genes to enhance drug synergy prediction.Bioinformatics (Oxford, England) · 2026Article
- Learning drug synergy through environment-conditioned feature modulation.Bioinformatics (Oxford, England) · 2026Article
- Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.iScience · 2026Article
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 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
- UniSyn: a multi-modal framework with knowledge transfer for anti-cancer drug synergy prediction.Genome biology · 2026Article
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
- Multi-modal contrastive drug synergy prediction model guided by single modality.Journal of cheminformatics · 2025Article
- SenolyticSynergy: An Attention-Based Network for Discovering Novel Senolytic Combinations via Human Aging Genomics.International journal of molecular sciences · 2025Article
- Accurate prediction of synergistic drug combination using a multi-source information fusion framework.BMC biology · 2025Article
- CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations.Frontiers in pharmacology · 2025Article
- PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.Frontiers in pharmacology · 2025Article
- DSA-DeepFM: a dual-stage attention-enhanced DeepFM model for predicting anticancer synergistic drug combinations.Bioinformatics advances · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug-Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations.
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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.