极化效应指数与脂肪酯类化合物沸点的定量构效关系研究

基于烷基极化效应指数(PEI)构建了有效碳原子数(N_Ceff)用于定量表示烷基碳链相对长度. 用最佳子集回归方法建立了脂肪酯沸点与PEI, N_Ceff参数的定量构效关系模型, 该模型对训练集计算值与实验值的相关系数R²为0.9958, 标准偏差s为3.98 K, 对测试集预测值与实验值的相关系数R²分别为: 0.9958, 标准偏差s为3.92 K, 计算结果表明基于参数PEI和N_Ceff所建立的脂肪酯类化合物的沸点定量构效关系模型具有良好的预测能力.     

Towards predicting the solubility of CO2 and N2 in different polymers using a quasi-SMILES based QSPR approach

The solubility of gases in various polymers plays an important role for the design of new polymeric materials. Quantitative structure–property relationship (QSPR) models were designed to predict the solubility of gases such as CO₂ and N₂ in polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl acetate (PVA) and poly (butylene succinate) (PBS) at different temperatures and pressures by using quasi-SMILES codes. The dataset of 315 systems was split randomly into training, calibration and validation sets; random split 1 led to 214 training (r² = 0.870 and RMSE = 0.019), 51 calibration (r² = 0.858 and RMSE = 0.020) and 50 validation (r² = 0.869 and RMSE = 0.017) sets. The suggested approach based on the quasi-SMILES, which are analogues of the traditional SMILES gives reasonable good predictions for solubility of CO₂ and N₂ in different polymers. The described methodology is universal for situations where the aim is to predict the response of an eclectic system upon a variety of physicochemical and/or biochemical conditions.     

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure–property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation , where S_w is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high‐efficiency QSPR model. The performance of the model is assessed using cross‐validation and external test set prediction. Predictive capacity of developed model is compared with COSMO‐RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.     

QSPR Modeling of Gibbs Free Energy of Organic Compounds by Weighting of Nearest Neighboring Codes

We examine the encoding of chemical structure of organic compounds by Labeled Hydrogen-Filled Graphs (LHFGs). Quantitative Structure-Property Relationships (QSPR) for a representative set of 150 organic molecules have been derived by means of the optimization of correlation weights of local invariants of the LHFGs. We have tested as local invariants Morgan extended connectivity of zero- and first order, numbers of path of length 2 (P2) and valence shells of distance of 2 (S2) associated with each atom in the molecular structure, and the Nearest Neighboring Codes (NNC). The best statistical characteristics for the Gibbs free energy has been obtained for the NNC weighting. Statistical parameters corresponding to this model are the following n = 100, r² = 0.9974, s = 5.136 kJ/mol, F = 38319 (training set); n = 50, r² = 0.9990, s = 3.405 kJ/mol, F = 48717 (test set). Some possible further developments are pointed out.