A QSAR Study of HIV Protease Inhibitors Using Theoretical Descriptors
| Autor: | Rajni Garg, Denise Mills, Subhash C. Basak, Barun Bhhatarai |
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| Rok vydání: | 2010 |
| Předmět: |
Models
Molecular Quantum chemical Principal Component Analysis Quantitative structure–activity relationship Models Statistical Variables Molecular Structure Rank (linear algebra) Chemistry Stereochemistry media_common.quotation_subject Quantitative Structure-Activity Relationship Experimental data Statistical model HIV Protease Inhibitors General Medicine Regression Drug Discovery Humans Regression Analysis Molecular Medicine Principal component regression Least-Squares Analysis Biological system media_common |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 1573-4099 |
| DOI: | 10.2174/1573409911006040269 |
| Popis: | This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective. |
| Databáze: | OpenAIRE |
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