Neural networks convergence using physicochemical data.

An investigation of the neural network convergence and prediction based on three optimization algorithms, namely, Levenberg-Marquardt, conjugate gradient, and delta rule, is described. Several simulated neural networks built using the above three algorithms indicated that the Levenberg-Marquardt optimizer implemented as a back-propagation neural network converged faster than the other two algorithms and provides in most of the cases better prediction. These conclusions are based on eight physicochemical data sets, each with a significant number of compounds comparable to that usually used in the QSAR/QSPR modeling.

Antimalarial activity: a QSAR modeling using CODESSA PRO software.

A quantitative structure-activity relationship (QSAR) modeling of the antimalarial activity of two diverse sets of compounds for each of two strains D6 and NF54 of Plasmodium falciparum is presented. The molecular structural features of compounds are presented by molecular descriptors (geometrical, topological, quantum mechanical, and electronic) calculated using the CODESSA PRO software. Satisfactory multilinear regression models were obtained for data sets of the D6 and NF54 strains, with R2 = 0.