RESUMO
Indole and its derivatives are common heterocyclic compounds in nature that have a wider range of medicinal activities such as antifungal, anti-inflammatory, and anti-seizure. Virtually all indole derivatives showed outstanding antifungal activity against Candida albicans. The aim of this study was to QSAR modeling of indole derivatives and the design of new drugs that have antifungal activity. In this study, 52 compounds were selected. All optimized compounds and quantum descriptors were obtained using Gaussian software and DFT/B3LYP computational method with 6-31 G (d) basis set al, so other descriptors were determined using Dragon software. To examine the relationship between these descriptors and the activity of these compounds, the MLR linear correlation method was used, and the QSAR equation with R2 = 0.7884 and R = 0.8879 was obtained for it. Likewise, MSE = 0.1897, RMSE = 0.2848, and Q2 = 0.68663 approve the acceptability of the obtained model. The obtained equation reveals that the activity of these compounds is related to the negative coefficient of GATS8p, R7e +, and G2e, which means that with increasing the values ââof these description nodes, the amount of activity declines. On the other hand, the activity of these compounds depended on the positive coefficients of HATS3p, MATS5e, and RDF045, i.e. with increasing these values, the activity of these compounds also increases, and a good correlation was obtained between the experimental and predicted activity values.
