Background/Objectives: Neglected tropical diseases (NTDs) caused by protozoan parasites such as Trypanosoma cruzi, Trypanosoma brucei, Leishmania spp., and Plasmodium falciparum remain a global health challenge due to limited therapies and increasing drug resistance. Natural products provide diverse scaffolds for antiparasitic drug discovery. This study aimed to investigate the multitarget inhibitory potential of alkaloids isolated from Croton linearis Jacq. against validated protozoan enzymes. Methods: Eighteen alkaloids were virtually screened against 17 molecular targets relevant to protozoan parasites. Protein–ligand docking simulations were performed using crystallographic structures of enzymes, including Cyp51, DHFR-TS, PTR1, AD-kinase, and DHODH. Predicted interactions were analyzed to identify hydrogen bonds, hydrophobic contacts, and π–π stacking with key residues in the active sites. Results: Several alkaloids exhibited high binding affinities, in some cases surpassing co-crystallized ligands. Reticuline, norsalutaridine, laudanosine, and jacularine consistently showed the strongest activity, with docking scores ranging from −8.0 to −9.3 kcal/mol across multiple targets. Notably, norsalutaridine displayed the highest predicted affinity for L. infantum Cyp51, while reticuline showed strong binding to T. cruzi DHFR-TS and L. major PTR1. Conclusions: The study highlights the potential of C. linearis alkaloids as multitarget inhibitors against protozoan parasites. These compounds represent promising lead candidates for the development of antiparasitic agents, while emphasizing the value of natural product scaffolds for neglected disease drug discovery. The findings also support the future exploration of semisynthetic derivatives to optimize activity and selectivity.

Background/Objectives: Leishmaniasis is a group of diseases caused by obligate intracellular parasites of the Leishmania genus and is classified by the World Health Organization as a category I neglected tropical disease. Leishmania infantum predominantly affects children under five years of age and shows an increasing incidence of cutaneous and visceral forms. The development of new therapeutic alternatives remains challenging, making in silico approaches valuable for accelerating antileishmanial drug discovery. This study aimed to identify new compounds with potential activity against Leishmania infantum amastigotes using artificial intelligence-based classification models. Methods: A curated database of compounds with reported biological activity was constructed. Molecular representation employed zero- to two-dimensional descriptors calculated with Dragon software (v 7.0.10). Unsupervised k-means cluster analysis was applied to define training and external prediction sets. Supervised models were developed on the WEKA platform using IBk, J48, multilayer perceptron, and sequential minimal optimization algorithms. Model performance was assessed through internal cross-validation and external validation procedures. Results: All models achieved classification accuracies above eighty percent for both training and prediction sets, indicating consistent predictive performance and good generalization ability. The validated models were applied to virtual screening of the DrugBank database and a collection of synthetic compounds. This screening campaign enabled the identification of one hundred twenty compounds with potential activity against the amastigote form of Leishmania infantum. Conclusions: Artificial intelligence-based QSAR models proved to be useful tools for prioritizing antileishmanial candidates. The integration of molecular descriptors, machine learning, and virtual screening offers an efficient strategy for drug discovery.