In Silico Prediction and Validation of LmGt Inhibitors Using QSAR and Molecular Docking Approaches

Abstract

Leishmaniasis caused by Leishmania mexicana relies on Leishmania mexicana gluscose transporter (LmGT) receptors, which play an important role in glucose and ribose uptake at different stages of parasite's life cycle. Previous efforts to identify LmGT inhibitors have been primarily based on in vitro screening. However, this conventional method is limited by inefficiency, high cost, and lack of specificity which leaves a significant gap in the development of targeted therapeutic candidates for LmGT. This study employs computational techniques to address this gap by developing a quantitative structure analysis relationship model, utilizing a support vector machine classifier to identify novel LmGt inhibitor. The QSAR model achieved an accuracy of 0.81 in differentiating active compounds. Molecular docking further validated the identified inhibitors, revealing strong binding affinities with a top score of -9.46. The docking analysis showed that the inhibitors formed multiple hydrogen bonds and occupied the same binding pockets as Phase 3 drug candidate. The tested inhibitors were derived from natural sources, which suggest reduced side effects and improved biocompability. This combined approach demonstrates the power of computational models in accelerating drug discovery, with implication for more efficient and biocompatible therapies against Leishmania mexicana.

Tasks

Reproductions