Abstract
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.
Keywords: Anti-HIV compounds, acquired immunodeficiency syndrome (AIDS), protease inhibitors, mathematical molecular descriptors, principal components regression, partial least squares, ridge regression, rank deficiency, immunodeficiency virus, retrovirus, Lentiviridae family, single-stranded RNA virus, opportunistic infections, neurological, neoplastic diseases, protein synthesis, immunological defense, mechanisms, apoptosis, mortality, morbidity of the disease, quantum chemical, linear solvation energy related, in silico evaluation, anti-protease activity, regression models, atom connectivity, topochemical (TC), protease enzyme inhibitory activity, fluorescent peptide substrate, high performance liquid chromatography, high quality QSARs, chemical-biological interaction, analyses, robust statistical methods, shrinkage methods, alternative regression methods, regression methodologies, cross validation, inflation, model validation techniques, topochemical descriptors, LinMods software, valence connectivity, van der Waals interaction, electrotopological index, internal hydrogen bonding, molecular framework, drug's biological activity, modeling methods, heuristic method
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