Genetic neural network modeling of the selective inhibition of the intermediate-conductance Ca2+-activated K+ channel by some triarylmethanes using topological charge indexes descriptors

Abstract

Selective inhibition of the intermediate-conductance Ca(2+)-activated K(+ )channel (IK (Ca)) by some clotrimazole analogs has been successfully modeled using topological charge indexes (TCI) and genetic neural networks (GNNs). A neural network monitoring scheme evidenced a highly non-linear dependence between the IK (Ca) blocking activity and TCI descriptors. Suitable subsets of descriptors were selected by means of genetic algorithm. Bayesian regularization was implemented in the network training function with the aim of assuring good generalization qualities to the predictors. GNNs were able to yield a reliable predictor that explained about 97% data variance with good predictive ability. On the contrary, the best multivariate linear equation with descriptors selected by linear genetic search, only explained about 60%. In spite of when using the descriptors from the linear equations to train neural networks yielded higher fitted models, such networks were very unstable and had relative low predictive ability. However, the best GNN BRANN 2 had a Q ( 2 ) of LOO of cross-validation equal to 0.901 and at the same time exhibited outstanding stability when calculating 80 randomly constructed training/test sets partitions. Our model suggested that structural fragments of size three and seven have relevant influence on the inhibitory potency of the studied IK (Ca) channel blockers. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (KSOM) built using the inputs of the best neural network predictor.

Modeling of the Inhibition of the Intermediate‐Conductance Ca2+‐Activated K+ Channel (IKCa1) by Some Triarylmethanes Using Quantum Chemical Properties Derived From Ab Initio Calculations

“…In a previous QSAR study, we identified the main characteristics of the TRAM analogs that made them more active by using topological charge indexes [25]. In this previous work, we concluded that the electronic content of these compounds can be used for derived structure -activity relationship in silico models.…”

“…In a recent report [25], we modeled the structureactivity relationship of this class of compounds by using topological charge indexes and bayesian-regularized genetic neural networks [26]. In that work, we concluded that molecular charge distribution plays an important role in IKCa1 inhibition.…”

Descriptor-and fragment-based QSAR models for a series of Schistosoma mansoni purine nucleoside inhibitors

“…Additional information regarding molecular charge and polarizability was also considered through the weighting of the descriptors. 28 A total of 2489 descriptors were calculated, and the highly correlated and those that convey no information towards the biological activity (constant and r 2 < 0.10) were excluded from further consideration. This protocol afforded 218 descriptors that were employed to build a number of preliminary QSAR models by multiple linear regression (MLR), containing up to 3 descriptors.…”

Modeling of the Inhibition Constant (Ki) of Some Cruzain Ketone‐Based Inhibitors Using 2D Spatial Autocorrelation Vectors and Data‐Diverse Ensembles of Bayesian‐Regularized Genetic Neural Networks

“…The autocorrelation vectors represent the degree of similarity between molecules. In addition, 2D spatial autocorrelation code has been successfully applied in nonlinear QSAR studies, proving to contain relevant nonlinear information concerning different biological phenomena [8,9,14,17].…”

“…The majority of the highly active KCIs (10,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,44,46), those that had highly hydrophobic P1' and P3 residues, were placed in Zone 1. Astonishingly, compound 43 with a carboxylic group in position R1 and a hydrogen at position R2, that means an inhibitor with Glycine in P1 residue and lacking of P1' residue but having high inhibition constant, was placed in this active neighborhood.…”