Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening.

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets.

Identification in silico and in vitro of novel trypanosomicidal drug-like compounds.

Atom-based bilinear indices and linear discriminant analysis are used to discover novel trypanosomicidal compounds. The obtained linear discriminant analysis-based quantitative structure-activity relationship models, using non-stochastic and stochastic indices, provide accuracies of 89.02% (85.