QSAR modelling of larvicidal phytocompounds against using index of ideality of correlation.

is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly larvicides against . The present study aimed to improve QSAR models for 62 larvicidal phytocompounds against via the Monte Carlo method based on the index of the ideality of correlation (IIC) criterion. The representation of structures was done with SMILES. Three splits were prepared randomly and three QSAR models were constructed using IIC target function. The molecular descriptors were selected from SMILES descriptors and the hydrogen-filled molecular graphs. The predictability of three models was evaluated on the validation sets, the of which was 0.9770, 0.8660, and 0.8565 for models 1 to 3, respectively. The statistical results of three randomized splits indicated that robust, simple, predictive, and reliable models were obtained for different sets. From the modelling results, important descriptors were identified to enhance and reduce the larvicidal activity of compounds. Based on the identified important descriptors, some new structures of larvicidal compounds were proposed. The larvicidal activity of novel molecules designed further was supported by docking studies. Using the simple QSAR model, one can predict pLC of new similarity larvicidal phytocompounds.