Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University , Tehran, Iran.
SAR QSAR Environ Res. 2020 Oct;31(10):717-739. doi: 10.1080/1062936X.2020.1806922.
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.
是几种感染性病毒的主要载体，这些病毒会引起黄热病、登革热、基孔肯雅热和寨卡热。最近，植物衍生产品已被测试为安全和环保的杀虫剂，以对抗 。本研究旨在通过基于理想相关指数 (IIC) 标准的蒙特卡罗方法改进针对 的 62 种杀幼虫植物化合物的 QSAR 模型。结构的表示是用 SMILES 完成的。随机准备了三个分裂，并使用 IIC 目标函数构建了三个 QSAR 模型。分子描述符是从 SMILES 描述符和填充氢的分子图中选择的。三个模型的可预测性在验证集上进行了评估，模型 1 到 3 的 值分别为 0.9770、0.8660 和 0.8565。三个随机分裂的统计结果表明，为不同的数据集获得了稳健、简单、可预测和可靠的模型。从建模结果中，确定了重要的描述符以增强和降低化合物的杀幼虫活性。基于鉴定出的重要描述符，提出了一些新的杀幼虫化合物结构。设计的新分子的杀幼虫活性进一步得到了对接研究的支持。使用简单的 QSAR 模型，可以预测新的类似杀幼虫植物化合物的 pLC。
