Validation subset selections for extrapolation oriented QSPAR models

One of the most important features of QSPAR models is their predictive ability. The predictive ability of QSPAR models should be checked by external validation. In this work we examined three different types of external validation set selection methods for their usefulness in in-silico screening. The usefulness of the selection methods was studied in such a way that: 1) We generated thousands of QSPR models and stored them in 'model banks'. 2) We selected a final top model from the model banks based on three different validation set selection methods. 3) We predicted large data sets, which we called 'chemical universe sets', and calculated the corresponding SEPs. The models were generated from small fractions of the available water solubility data during a GA Variable Subset Selection procedure. The external validation sets were constructed by random selections, uniformly distributed selections or by perimeter-oriented selections. We found that the best performing models on the perimeter-oriented external validation sets usually gave the best validation results when the remaining part of the available data was overwhelmingly large, i.e., when the model had to make a lot of extrapolations. We also compared the top final models obtained from external validation set selection methods in three independent and different sizes of 'chemical universe sets'.     

Neural Network Based on Quantum Chemistry for Predicting Melting Point of Organic Compounds

采用反向传播神经网络与定量结构-性质关系中量子化学参数相结合的方法测定有机化合物的熔点．11个反映分子间作用力和分子对称性的描述符作为输入变量,通过分子模型和PM3半经验分子轨道理论计算量子化学参数．用260个化合物训练由MatLab方法建立的神经网络,预测了73个化合物的熔点,并与文献中的实验数据进行比较,结果表明这种人工神经网络与定量结构-性质关系结合的方法可以预测有机化合物的熔点,平均绝对偏差5%．     

Improved model for fullerene C<Subscript>60</Subscript> solubility in organic solvents based on quantum-chemical and topological descriptors

Fullerenes are sparingly soluble in many solvents. The dependence of fullerene’s solubility on molecular structure of the solvent must be understood in order to manage efficiently this class of compounds. To find such dependency ab initio quantum-chemical calculations in combination with quantitative structure–property relationship (QSPR) tool were used to model the solubility of fullerene C₆₀ in 122 organic solvents. A genetic algorithm and multiple regression analysis (GA-MLRA) were applied to generate correlation models. The best performance is accomplished by the four-variable MLRA model with prediction coefficient r _test² = 0.903. This study reveals a correlation of highest occupied molecular orbital energy (HOMO), certain heteroatom fragments, and geometrical parameters with solubility. Several other important parameters of solvents that affect the C₆₀ solubility have been also evaluated by the QSPR analysis. The employed GA-MLRA approach enhanced by application of quantum-chemical calculations yields reliable results, allowing one to build simple, interpretable models that can be used for predictions of C₆₀ solubility in various organic solvents.     

聚丙烯酸酯玻璃化转移温度QSPR的研究

对聚丙烯酸酯的定量构性关系(QSPR)研究具有重要意义。采用分子电性作用失量(MEIV)表征聚丙烯酸酯的分子结构,运用多元线性回归(MLR)建立定量结构玻璃化转移温度相关(QSPR)模型,同时采用逐步回归结合统计检测筛选模型变量,建立了22个聚丙烯酸酯玻璃化转移温度（Tg）与其结构间的多元线性回归方程。另外采用内部及外部双重验证的办法深入分析和检验模型的稳定性。建模的复相关系数(Rcum)、留一法(LOO)交互校验复相关系数(Qcum)和外部样本校验复相关系数(Qext)分别为0.982、0.971和0.922。表明用MEIV对聚丙烯酸酯分子结构信息表达较好,所建QSPR模型的稳定性和预测能力良好。     

应用键轨道连接矩阵方法预测烷烃折光率.

应用键轨道连接矩阵方法提取结构参数PX1CC和PX1CH， 并用于建立预测烷烃折光率的QSPR模型．该模型不仅预测精度较高(其误差仅为0.0048)，而且模型中采用的参数表达了直接与分子的折光能力相关的结构信息(即烷烃分子中电子被极化的能力)．此参数提取简单，既具备拓扑指数简单、易算，物理意义明确．