Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India.
Department of Chemistry, Kurukshetra University, Kurukshetra, India.
SAR QSAR Environ Res. 2021 Oct;32(10):817-834. doi: 10.1080/1062936X.2021.1973095. Epub 2021 Sep 17.
Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF without IIC and TF with IIC). Eight QSPR models were developed and the models developed with TF was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were ; ; ; ; ; ; ; ; IIC = 0.8590; IIC = 0.8297; IIC = 0.8796; IIC = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.
简化分子输入行-entry 系统（SMILES）符号和 CORAL 软件的内置蒙特卡罗算法被用于构建 215 个吩噻嗪衍生物的功率转换效率（PCE）分析的生成和预测 QSPR 模型。数据集被分为四个部分，每个部分进一步分为四个部分。混合描述符，SMILES 和氢抑制图（HSG）的组合被用于构建可靠和强大的 QSPR 模型。还深入研究了理想相关指数（IIC）的指标的作用。我们进行了比较研究，使用两个目标函数（无 IIC 的 TF 和带 IIC 的 TF）来预测 PCE。开发了八个 QSPR 模型，并且显示带 TF 的模型是鲁棒和可靠的。来自第 4 部分的 QSPR 模型被认为是主导模型。针对主导模型计算了不同的统计基准，包括；；；；；；；；IIC=0.8590；IIC=0.8297；IIC=0.8796；IIC=0.8293 等。还提取了增加和减少端点 PCE 的促进剂。
