Pattern recognition techniques applied to the study of leishmanial glyceraldehyde-3-phosphate dehydrogenase inhibition.

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase.

Quantitative structure-activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4.

Molecular features for antitrypanosomal activity of thiosemicarbazones revealed by OPS-PLS QSAR studies.

A quantitative structure-activity relationship analysis was employed to explore the relationship between the molecular structure of thiosemicarbazone analogues and the inhibition of the cysteine protease cruzain, a validated target for Chagas' disease treatment. A data set containing 53 thiosemicarbazone derivatives was used to produce a quantitative model for activity prediction of unknown compounds. Several electronic descriptors were obtained through DFT calculations, along with a large amount of Dragon descriptors.