ArticleInternational journal of molecular sciences2011
A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest.
Article in International journal of molecular sciences, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Microbiological and epidemiological features of respiratory syncytial virus.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2024Review
- Rhizoma Atractylodis Macrocephalae-Assessing the influence of herbal processing methods and improved effects on functional dyspepsia.Frontiers in pharmacology · 2023Article
- Prediction of skin sensitization potency using machine learning approaches.Journal of applied toxicology : JAT · 2017Article
- Multivariate models for prediction of human skin sensitization hazard.Journal of applied toxicology : JAT · 2017Article
- Classification of HCV NS5B polymerase inhibitors using support vector machine.International journal of molecular sciences · 2012Article
- Toward the prediction of FBPase inhibitory activity using chemoinformatic methods.International journal of molecular sciences · 2012Article
- A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.PloS one · 2012Article
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Authors and funding
4 authors.
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Abstract
Experimental pEC(50)s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.
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