Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints

Leishmaniasis, a neglected tropical disease caused by Leishmania protozoan parasites and transmitted by sandflies, poses a significant global health challenge, especially in resource-limited environments. The life cycle of the parasite includes crucial amastigote and promastigote stages, each contributing importantly to the infection process. The current therapies for leishmaniasis face limitations due to considerable side effects and the rise of drug-resistant strains, underscoring the pressing need for new, effective, and safe treatment options. Recent advancements in leishmaniasis vaccine development include live attenuated vaccines, recombinant vaccines, and the use of synthetic biology. These approaches aim to induce robust immune responses while ensuring safety. Controlled human infection studies are also being explored to accelerate vaccine development. However, a licensed vaccine remains elusive.

This study introduces a novel method for drug discovery targeting leishmaniasis, employing machine learning and cheminformatics to forecast the efficacy of compounds against Leishmania promastigotes. A detailed dataset consisting of 65,057 molecules sourced from the PubChem database is utilized, with the Alamar Blue-based assay applied to assess drug susceptibility. The data encoding relies on molecular fingerprints derived from Simplified Molecular Input Line Entry System (SMILES) notations. We employed three distinct fingerprint algorithms, Avalon, MACCS Key, and Pharmacophore, for the development of machine learning models. Various algorithms, including random forest, multilayer perceptron, gradient boosting, and decision tree, are utilized to create models that effectively classify molecules as either active or inactive based on their structural and chemical characteristics, which could significantly impact the drug discovery process for leishmaniasis.

We additionally introduced a model based on ensembles, achieving a peak accuracy of 83.65% and an area under the curve of 0.8367. This study offers significant promise in enhancing drug discovery efforts focused on tackling the global issue of leishmaniasis.

Furthermore, the proposed approach has the potential to serve as a framework for addressing other overlooked tropical diseases, offering a promising alternative to conventional drug discovery methods and their associated difficulties.