Abstract
Leishmaniasis is a parasitic disease caused by infection with Leishmania parasites, which are spread by the bite of phlebotomine sand flies. Quinoline derivatives have shown potential as antileishmanial agents. However, it is important to note that quinoline derivatives are still in the research phase, and their clinical use for leishmaniasis treatment is not yet established. This study aimed to conduct QSAR modeling of quinoline derivatives and develop new drugs with anti leishmania properties. A total of 52 compounds were carefully chosen for this study. The optimized compounds and quantum descriptors were obtained using Gaussian software and the DFT/B3LYP computational method with a 6-31G (d) basis set. Other descriptors were determined using Dragon software. To analyze the relationship between these descriptors and the activity of the compounds, the MLR linear correlation method was employed. As a result, a QSAR equation with an R2 = 0.74 and R=0.86 was derived. The model's acceptability was further confirmed by the values of RMSE (0.48), and Q2 (0.62). The obtained equation indicates that the negative coefficients of MATS1v, GATS6m and HATS7u influence the activity of these compounds. This implies that as these descriptors' values increase, the compounds' activity decreases. Conversely, the activity of these compounds is influenced by the positive coefficients of HATS8e, R5u+, and G2u. In other words, as these descriptor values increase, the activity of the compounds also increases. This correlation between the experimental and predicted activity values demonstrates a strong relationship.