ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- GSDME-mediated pyroptosis modulates the immunosuppressive microenvironment in lung adenocarcinoma.Acta pharmacologica Sinica · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Metapath2vec and attention-driven heterogeneous graph learning for prioritizing TCM-derived small molecules in gastric cancer.Chinese medicine · 2026Article
- Febrifugine dihydrochloride restricts porcine epidemic diarrhea virus replication by modulating the IGF1R-driven PI3K/AKT-apoptosis axis.Journal of virology · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- Transfer Learning and Permutation-Invariance Improving Predicting Genome-Wide, Cell-Specific and Directional Interventions Effects of Complex Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Target identification of natural products in cancer with chemical proteomics and artificial intelligence approaches.Cancer biology & medicine · 2025Review
- A computational-based new treatment strategy with three-armed RCT on Mycoplasma pneumoniae pneumonia in children.Chinese medicine · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases. Furthermore, this model effectively addresses the challenge of balancing large-scale positive and negative samples, leading to improved performance across various evaluation metrics such as an Area under curve (AUC) of 0.9298 and an F1 score of 0.6316. Moreover, the algorithm accurately predicts drug combinations and achieves an F1 score of 0.7746 after fine-tuning. Additionally, it identifies two previously unexplored synergistic drug combinations for distinct cancer types in disease-specific biological network environments. These findings are further validated through in vitro cytotoxicity assays, demonstrating the potential of the model to enhance drug development and identify effective treatment regimens for specific diseases.
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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.