Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds.

Malaria is one of the most deadly diseases, affecting million of people especially in developing countries. Because of the rapidly increasing threat worldwide of malaria epidemics multidrugs resistant to therapies, there is an urgent global need to discover new classes of antimalarial compounds. In an effort to overcome this problem, we have investigated the use of structure-based classification models for the 'rational' selection/identification or design/optimization of new lead antimalarials from virtual combinatorial data sets. In this sense, TOpological MOlecular COMputer Design strategy (TOMOCOMD approach) has been introduced in order to obtain two quantitative models for the discrimination of antimalarials. A collected data set containing 597 antimalarial compounds is presented as a helpful tool not only for theoretical chemist but for other researchers in this area. The validated models (including non-stochastic and stochastic indices) classify correctly more than 90% of compounds in both training and external prediction data sets. They showed high Matthews' correlation coefficients; 0.87 and 0.82 for training and 0.86 and 0.79 for test set. The TOMOCOMD-CARDD approach implemented in this work was successfully compared with two of the most useful models for antimalarials selection reported so far. Thus we expect that these two QSAR models can be used in the identification of previously un-known antimalarials compounds.

A computer-based approach to the rational discovery of new trichomonacidal drugs by atom-type linear indices.

Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.