基于GA-MLR方法的有机化合物热膨胀系数的QSPR研究

利用QSPR研究方法,结合遗传算法(Genetic Algorithm,GA)和多元线性回归(Multiple Linear Regression,MLR),建立了3635个有机化合物液相膨胀系数的QSPR模型。该模型包含6个描述符,对于训练集R2=0.833%,Q2=0.810%,RMSE和AARD分别是0.043%和1.02%,测试集的统计结果是R2=0.811,RMSE=0.061%,AARD=1.425%。这个模型对于包含大量有机化合物的数据集来说,是可靠并稳定的,具有较好的预测能力。     

聚氧乙烯类和硫酸盐类表面活性剂HLB值的研究

亲水亲油平衡值(Hydrophile/Lipophile Balance,HLB)是表征表面活性剂性质的重要参数。采用比较分子力场分析(comparative molecular field analysis,CoMFA)方法和比较分子相似性指数分析(comparative similarity indices analysis,CoMSIA)方法,研究了30个烷基酚聚氧乙烯醚系列非离子表面活性剂的三维定量结构与pHLB值的关系。建立的CoMFA模型交叉验证系数为0.705,非交叉验证系数为0.805:CoMSIA模型交叉验证系数为0.685,非交叉验证系数为0.813,都有较好的预侧能力。此外,运用多元线性回归方法建立一种预报硫酸盐类表面活性剂HLB的QSPR模型,模型的复相关系数为0.711。所有选取的测试集和训练集的预报结果较为吻合,说明硫酸盐类表面活性剂HLB值QSPR模型具有良好的预报能力和普适性。     

QSPR在有机物燃烧特性预测中的应用和进展

定量结构-性质相关性(QSPR)研究将有机物结构特征表征方法和各种统计建模工具相结合,研究有机物结构与其各种性质之间的内在关系.它不仅可以揭示物质性质与分子结构之间的定量函数关系,而且为工程上提供预测有机物性质的有效方法,因此在众多领域得到了广泛的应用.阐述了QSPR研究基本原理,论述了其在闪点、自燃点、爆炸极限等化学物质燃烧特性预测中的应用和进展,并对各性质的不同预测模型进行了比较,分析其优缺点及适用范围.对实验样本设计、分子结构表征及建模方法选择等的研究现状和发展趋势进行了讨论,提出了QSPR在安全科学研究中的应用前景和发展方向.     

有机物临界体积的定量构性

采用分子模拟和多元线性回归方法,研究了有机物临界体积的定量结构-性质关系(QSPR).基于121个临界体积数据中的108个样本点,得到一个临界体积模型(残余标准差11.80 cm3/mol,拟合度0.994 2).该模型含4个分子描述码,对训练组和测试组的平均估算误差分别为8.36cm3/mol(相对误差2.52%)和9.09 cm3/mol(相对误差3.05%).研究表明,分子体积、分子支链化程度以及分子表面的静电分布等定量结构参数可以有效地估算有机物的临界体积.     

An Application of Computer‐Aided Molecular Design (CAMD) Using the Signature Molecular Descriptor–Part 2. Evaluating Newly Identified Surface Tension‐Reducing Substances for Potential Use as Shrinkage‐Reducing Admixtures

In this study, the use of computer‐aided molecular design (CAMD) is validated as a tool for enabling the discovery of new shrinkage‐reducing compounds for possible use in portland cement composites and is framed as one of many multiscale modeling tools in a broad hierarchy of possibilities. Twelve additives were tested for their ability to inhibit shrinkage in Type I ordinary portland cement under both autogenous and drying conditions. The 12 additives included two commercial shrinkage‐reducing admixtures (SRAs), two active ingredients of a commercial admixture [one of which was used to establish the quantitative structure–property relationships (QSPR)], two additional classified as potential SRA compounds based on the patent literature, four newly identified compounds predicted by using CAMD and an inverse quantitative structure–property relationship (I‐QSPR), and two other compounds use to establish the QSPR relationship. The newly identified I‐QSPR compounds were targeted for their ability to reduce the surface tension of water, a primary consideration for shrinkage‐reducing activity. Results for both drying shrinkage and autogenous shrinkage indicate that the designed compounds perform similar to commercial admixtures, yet have different chemical functionalities. Hydration data and set measurements were also considered since selection of new SRAs is a multiparameter problem with many constraints. Thus, these newly identified shrinkage‐reducing compounds can potentially provide additional options for use in portland cement concrete applications.