ArticlePloS one2012
A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 189 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
189 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A critical assessment of Traditional Chinese Medicine databases as a source for drug discovery.Frontiers in pharmacology · 2024Pooled it
- Network pharmacology-based analysis of the antithrombotic clinical efficacy and antithrombotic mechanism of Huoxue Jiedu prescription in the treatment of polycythemia vera with heat toxin and blood stasis syndrome.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Trial
- Network Pharmacology-Based Exploration of Complementary Molecular Mechanisms of Heat-Clearing (Scutellariae Radix, Coptidis Rhizoma) and Blood-Tonifying Herbs (Angelicae Sinensis Radix, Paeoniae Radix Alba) in Cold Hypersensitivity in Hands and Feet.Life (Basel, Switzerland) · 2026Article
- Article
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- Advancing the modernization of traditional Chinese medicine through artificial intelligence and multimodal data integration.Chinese medicine · 2026Review
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Heterogeneous biological graph convolutional network for drug-target interaction prediction.PloS one · 2026Article
- Advances in Traditional Chinese Medicine for chronic obstructive pulmonary disease through multi-omics approaches.Frontiers in cell and developmental biology · 2026Review
- Evaluating current status of network pharmacology for herbal medicine focusing on identifying mechanisms and therapeutic effects.Journal of advanced research · 2025Article
- Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery.EJNMMI research · 2025Article
- AI-Driven Multiscale Study on the Mechanism of Polygonati Rhizoma in Regulating Immune Function in STAD.ACS omega · 2025Article
- Deciphering the anti‑influenza potential of Eucommiae Cortex based on bioinformatics analysis:Experimental and therapeutic medicine · 2025Article
- Decoding herbal combination models through systematic strategies: insights from target information and traditional Chinese medicine clinical theory.Briefings in bioinformatics · 2025Article
- APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions.Journal of cheminformatics · 2025Article
- Network Pharmacology Combined with Animal Models to Investigate the Mechanism of ChangPu YuJin Tang in the Treatment of Tourette Syndrome.Combinatorial chemistry & high throughput screening · 2025Article
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- A Gut Microbial Transformation-Integrated Network Pharmacology Approach to Elucidate the Therapeutic Mechanisms of Timosaponin AIII in Diabetes.Current drug metabolism · 2025Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
129 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.
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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.