a Department of Chemistry , Kurukshetra University , Kurukshetra , Haryana , India.
b Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , Haryana , India.
SAR QSAR Environ Res. 2019 Feb;30(2):63-80. doi: 10.1080/1062936X.2018.1564067.
Quantitative structure-activity relationship (QSAR) modelling of 55 focal adhesion kinase (FAK) (EC 2.7.10.2) inhibitors of triazinic nature was performed using the Monte Carlo method. The QSAR models were designed by CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES). Four splits were made from the triazinic derivative data by random division into training, invisible training, calibration and validation sets. The QSAR results from these four random splits were robust, very simple, predictive and reliable. The best statistical parameters of the validation set (r = 0.8398 and Q = 0.7722) for the QSAR equation for split 3 with IIC = 0.9127 were obtained. The predictive potential of QSAR models of FAK inhibitors was explored by applying the index of ideality of correlation (IIC), which is a new criterion for the prediction of the potential for quantitative structure-property activity relationships (QSPRs/QSARs). The present method follows OECD principles.
采用蒙特卡罗方法对 55 种具有三唑结构的粘着斑激酶（FAK）（EC 2.7.10.2）抑制剂进行了定量构效关系（QSAR）建模。QSAR 模型由 CORAL 软件设计，最优描述符采用简化分子线性输入系统（SMILES）计算。通过随机分割将三唑衍生物数据分为训练集、不可见训练集、校准集和验证集。这四个随机划分的 QSAR 结果具有稳健性、非常简单、可预测和可靠性。在 IIC = 0.9127 的情况下，对于 3 号分裂的 QSAR 方程，验证集的最佳统计参数（r = 0.8398 和 Q = 0.7722）。通过应用理想相关指数（IIC）来探索 FAK 抑制剂 QSAR 模型的预测潜力，这是一种用于预测定量结构-性质-活性关系（QSPRs/QSARs）潜力的新标准。本方法遵循 OECD 原则。
