Chagas disease affects 8–11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomena have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we compiled and curated a database of diverse cruzain inhibitors previously reported in the literature. From this data set, quantitative structure–activity relationship (QSAR) models for the prediction of their pIC 50 values were generated using k -nearest neighbors and random forest algorithms. Local and global models were calculated and compared. The statistical parameters for internal and external validation indicate a significant predictability, with q loo 2 values around 0.66 and 0.61 and external R 2 coefficients of 0.725 and 0.766. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage and similarity methods. The models described in this work are readily available in a Python script for the discovery of novel cruzain inhibitors.

Chagas disease affects 8-11 million people worldwide, most of them living in Latin America. Moreover, migratory phenomenon have spread the infection beyond endemic areas. Efforts for the development of new pharmacological therapies are paramount, as the pharmacological profile of the two marketed drugs currently available, nifurtimox and benznidazole, needs to be improved. Cruzain, a parasitic cysteine protease, is one of the most attractive biological targets due to its roles in parasite survival and immune evasion. In this work, we generated Quantitative Structure-Activity Relationship linear models for the prediction of pIC 50 values of cruzain inhibitors. The statistical parameters for internal and external validation indicate high predictability with a cross-validated correlation coefficient of q 2 cv = 0.77 and an external correlation coefficient of r 2 ex = 0.71. The applicability domain is quantitatively defined, according to QSAR good practices, using the leverage method. A qualitative interpretation of the model is provided based on protein-ligand interactions obtained from docking studies and structural information codified in the molecular descriptors relevant to the QSAR