QSAR Modeling of PeripheralVersusCentral Benzodiazepine Receptor Binding Affinity of 2‐Phenylimidazo[1,2‐a]pyridineacetamides using Optimal Descriptors Calculated with SMILES
Kunal Roy, Andrey Andreevich Toropov, Ivan Raška · QSAR & Combinatorial Science · 2006
Abstract The optimization of correlation weights scheme has been applied to model Peripheral Benzodiazepine Receptor (PBR) binding affinity (ovary and cortex) and PBR binding selectivity (peripheralversuscentral benzodiazepine receptor) of 2‐phenylimidazo[1,2‐a]pyridineacetamides. In the present study, optimal descriptors based on Simplified Molecular Input Line Entry System (SMILES) notation have been used for the modeling purpose. The optimized descriptor formulated based on the data of training set generated statistically acceptable relations (for PBR cortex,q2=0.717,r2=0.756; for PBR ovary,q2=0.836,r2=0.852; for PBR cortex selectivity,q2=0.732,r2=0.784; for PBR ovary selectivity,q2=0.828,r2=0.845). When the relations of PBR binding affinity or selectivity with the optimized molecular descriptor formulated based on the data of the training set was used for the calculation of the corresponding response parameters of the test set,$\rm{ r_{{\rm{Pred}}}^{\rm{2}} }$values were found to be satisfactory (for PBR cortex, 0.692; for PBR ovary, 0.682; for PBR ovary selectivity, 0.772) except in the case of PBR cortex selectivity ($\rm{ r_{{\rm{Pred}}}^{\rm{2}} }$being 0.321). The results indicate promising potential of the optimization of correlation weights based on SMILES notation in modeling studies.