ArticlePloS one2013
iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 117 citations in OpenAlex.
- EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.BMC biology · 2025Article
- Identify potential drug candidates within a high-quality compound search space.Briefings in bioinformatics · 2024Article
- Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.Molecules (Basel, Switzerland) · 2023Article
- HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.BMC bioinformatics · 2022Article
- SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.Frontiers in public health · 2022Article
- DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.Frontiers in genetics · 2022Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches.Scientific reports · 2020Article
- Identifying GPCR-drug interaction based on wordbook learning from sequences.BMC bioinformatics · 2020Article
- BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR-Drug Interaction Based on Wordbook Learning From Sequences.Frontiers in cell and developmental biology · 2020Article
- Current Biochemical Applications and Future Prospects of Chlorotoxin in Cancer Diagnostics and Therapeutics.Advanced pharmaceutical bulletin · 2019Review
- Revealing Drug-Target Interactions with Computational Models and Algorithms.Molecules (Basel, Switzerland) · 2019Review
- Plant protection product dose rate estimation in apple orchards using a fuzzy logic system.PloS one · 2019Article
- Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs.Medicinal chemistry (Shariqah (United Arab Emirates)) · 2019Review
- Detecting Succinylation sites from protein sequences using ensemble support vector machine.BMC bioinformatics · 2018Article
- A proteome-wide systems toxicological approach deciphers the interaction network of chemotherapeutic drugs in the cardiovascular milieu.RSC advances · 2018Article
- Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.Pharmaceuticals (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.
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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.