A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines toward α₁ Adrenoceptors

Abstract

The genetic function approximation (GFA) algorithm has been used to derive a three-term QSAR equation able to correlate the structural properties of arylpiperazine derivatives with their affinity toward the alpha1 adrenoceptor (alpha1-AR). The number of rotatable bonds, the hydrogen-bond properties, and a variable belonging to a topological family of descriptors (chi) showed significant roles in the binding process toward alpha1-AR. The new model was also compared to a previous pharmacophore for alpha1-AR antagonists and a QSAR model for alpha2-AR antagonists with the aim of finding common or different key determinants influencing both affinity and selectivity toward alpha1- and alpha2-AR.

Feature Selection Methods in QSAR Studies

“…Progeny equations can then be generated, and the fitness of each progeny equation can be assessed by the LOF measure. When the fitness of the new progeny equation is better, it is preserved (104,105). An improvement of GA as feature selection is done in (106) by adding an extra step after selection, cross-over, and mutation.…”

Chemometrics tools in QSAR/QSPR studies: A historical perspective

“…adrenoceptors [469], Human protein tyrosine phosphatase 1B inhibitors [470], potent human protein tyrosine phosphatase inhibitors [471], some thiourea derivatives as influenza virus neuraminidase inhibitors [472] and also predicting the ecotoxicity of ionic liquids [473]. The potential of GFA makes this algorithm as an interesting option of utilization in a lot of QSAR/QSPR reports during these years [74,469,.…”

“…The potential of GFA makes this algorithm as an interesting option of utilization in a lot of QSAR/QSPR reports during these years [74,469,.…”

A General Model of Impact Sensitivity for Nitrogen‐Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study

“…To perform this study, we have combined our recently developed incremental theory of impact sensitivity [33] and a powerful regression method, namely, genetic function approximation (GFA). [34] This method is based on the evolutional algorithms [35] and is used mainly in the biochemical studies; [36][37][38][39] however, we have recently applied GFA for prediction of friction sensitivity [40] and aquatic toxicity [41] of energetic materials and obtained very good agreements with the experimental data. This method demonstrates a good resistance to overfitting due to the use of Friedman Lack-of-Fit as a scoring function.…”