A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

统计方法校正O3LYP方法计算的生成热

由于引入各种内在近似, 密度泛函理论存在固有误差. 本文采用O3LYP/6-311+G(3df, 2p)//O3LYP/6-31G(d)计算了220个中小型有机分子的生成热(Δ_fH_calc^Θ), 随后应用神经网络(ANN)和多元线性回归(MLR)方法对Δ_fH_calc^Θ进行校正. 采用计算得到的生成热、零点能、分子中原子总数、氢原子个数、双中心成键电子数、双中心反键电子数、单中心价层孤对电子数、单中心内层电子数作为ANN和MLR的描述符. 以180个分子作为训练集构造ANN或MLR模型, 并对40 个独立测试集分子的Δ_fH_calc^Θ进行了预测. 结果表明: 经过ANN和MLR校正后,训练集分子生成热的理论计算值和实验值间的均方根偏差(RMSD)从24.7 kJ·mol^-1分别降低到11.8、13.0 kJ·mol^-1; 独立测试集分子的RMSD从21.3 kJ·mol^-1分别降低到10.4、12.1 kJ·mol^-1. 因此ANN模型的拟合和预测能力要明显优于MLR模型.