ArticlePloS one2010
Predicting drug-target interaction networks based on functional groups and biological features.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
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Who cites it
93 citing papers in PubMed.
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- Advancements in small molecule drug design: A structural perspective.Drug discovery today · 2023Review
- A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.International journal of molecular sciences · 2023Article
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- Antibacterial Properties and Computational Insights of Potent Novel Linezolid-Based Oxazolidinones.Pharmaceuticals (Basel, Switzerland) · 2023Article
- MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.Molecules (Basel, Switzerland) · 2023Article
- Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.Journal of cheminformatics · 2022Article
- BitterMatch: recommendation systems for matching molecules with bitter taste receptors.Journal of cheminformatics · 2022Article
- DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.Frontiers in genetics · 2022Article
- SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19.Frontiers in public health · 2022Article
- EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.Biomolecules · 2021Article
- Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.BMC bioinformatics · 2021Article
- Predicting Drug-Target Interactions Based on the Ensemble Models of Multiple Feature Pairs.International journal of molecular sciences · 2021Article
- The pursuit of mechanism of action: uncovering drug complexity in TB drug discovery.RSC chemical biology · 2021Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudy of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. METHODS/PRINCIPAL
findingsTo realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. CONCLUSION/SIGNIFICANCE: Our results indicate that the network prediction system thus established is quite promising and encouraging.
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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.