ArticleMolecular diversity2011
QSAR study of C allosteric binding site of HCV NS5B polymerase inhibitors by support vector machine.
Article in Molecular diversity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Anti-gastric cancer activity of 1,2,3-triazolo[4,5-d]pyrimidine hybrids (1,2,3-TPH): QSAR and molecular docking approaches.Heliyon · 2020Article
- HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors.Journal of cheminformatics · 2018Article
- QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer.Iranian journal of pharmaceutical research : IJPR · 2017Article
- Article
- Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.Molecular diversity · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple linear regressions (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as chemical, topological, geometrical, and quantum descriptors. Principal component analysis (PCA) was used to select the training set. A variable selection method utilizing a genetic algorithm (GA) was employed to select from the large pool of calculated descriptors, an optimal subset of descriptors which have significant contribution to the overall inhibitory activity. The models were validated using Leave-One-Out (LOO) and Leave-Group-Out (LGO) crossvalidation, and Y-randomization test. Results demonstrated the SVM model offers powerful prediction capabilities.
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Identifiers
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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.