Novel atomic-level-based AI topological descriptors: application to QSPR/QSAR modeling

Novel atomic level AI topological indexes based on the adjacency matrix and distance matrix of a graph is used to code the structural environment of each atomic type in a molecule. These AI indexes, along with Xu index, are successfully extended to compounds with heteroatoms in terms of novel vertex degree v(m), which is derived from the valence connectivity delta(v) of Kier-Hall to resolve the differentiation of heteroatoms in molecular graphs. The multiple linear regression (MLR) is used to develop the structure-property/activity models based on the modified Xu and AI indices. The efficiency of these indices is verified by high quality QSPR/QSAR models obtained for several representative physical properties and biological activities of several data sets of alcohols with a wide range of non-hydrogen atoms. The results indicate that the physical properties studied are dominated by molecular size, but other atomic types or groups have small influences dependent on the studied properties. Among all atomic types, -OH groups seem to be most important due to hydrogen-bonding interactions. On the contrary, -OH groups play a dominant role in biological activities studied, although molecular size is also an important factor. These results indicate that both Xu and AI indices are useful model parameters for QSPR/QSAR analysis of complex compounds.     

Simplex representation of molecular structure as universal QSAR/QSPR tool

Structural Chemistry - We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its...     

Toward a universal quantum QSPR operator

This article presents a discussion about the formalism, which might be associated to a general Quantum quantitative structure–properties relations operator, appearing in a Boltzmann‐like exponential form, which is based in turn on the definition of the concept of thermal voltage, applied to thermally scaled electronic density functions. Three practical numerical examples are presented, corresponding to the calculation of the polarization angle in assorted chiral molecules, the estimation of fish toxicity for perchlorobenzene within the set of chlorobenzenes and a typical quantum QSAR study on the popular Cramer steroid set.     

表面活性剂的QSAR/QSPR研究进展*

本文综述了表面活性剂定量结构-活性/性质相关(QSAR/QSPR)研究的最新进展,以及相关结构描述符在表面活性剂QSAR/QSPR研究中的应用.重点介绍了表面活性剂的cmc和表面张力γ与分子结构的定量关系、离子型表面活性剂电荷分布的定量计算及其对胶体结构与性质的影响.对表面活性剂分子结构与活性/性质的定量相关研究的发展趋势进行了展望.     

Chromatographic descriptors in QSAR study of barbiturates

Barbiturate derivatives were evaluated for their parameters of biological activity by applying linear regression and two multivariate methods (Cluster analysis and Principal component analysis). The lipophilicity of the studied barbiturates was determined on the modified carriers C18 in mixtures of water and four organic modifiers separately (methanol, n-propanol, acetone and tetrahydrofuran) by performing reversed phase thin layer chromatography and by applying relevant software packages. Chromatographic and computational lipophilicity of the examined barbiturates was correlated with the selected pharmacokinetic and toxicological predictors and good relationships were obtained. More concrete results were obtained by multivariate methods which showed that the polarity of the substituent has the greatest influence, and its electronic effects to a lesser extent on the tested parameters of the barbiturate derivatives. Results obtained by multivariate methods also suggest that the chromatographic retention constant, R_M⁰, shows a greater resemblance to the parameters of lipophilicity. The chromatographic parameter m, exhibits better agreement with the toxicity parameters.     

A Lu index for QSAR/QSPR studies

A novel topological index based on Wiener index, named Lu index, is proposed and can be used for modeling properties of heteroatom-containing and multiple bond organic compounds. The usefulness of the Lu index was demonstrated by modeling normal boiling points and molar refractions of aldehydes and ketones, and the retention indices of gas chromatography of hydrocarbons and halogenated hydrocarbons. The high correlation coefficients (>0.99) for all constructed models indicate the need of such an index, and show the potential of the Lu index for QSAR/QSPR studies.     

离子液体的定量结构-性质/活性研究

本文系统介绍了离子液体定量结构-性质/活性相关(QSPR/QSAR)的研究方法和步骤,综述了QSPR/QSAR在离子液体的熔点、有机物在离子液体中的无限稀释活度系数、离子液体的表面张力、离子液体的电导率、有机物在离子液体中的溶解度、离子液体的黏度以及离子液体的生物毒性和降解性等方面的最新研究进展,总结了该方法的优缺点,并对未来的研究趋势进行了展望。     

QSAR and QSPR studies of a highly structured physicochemical domain

The relevance of terms other than linear when deriving quantitative structure-activity relationship/quantitative structure-property relationship (QSAR/QSPR) models has been rarely considered so far. In this study, the impact of quadratic and interacting terms has been taken into account. The first effect of including such highly structured terms is a significant extension of the parametric domain that moves from the initial N to N(N + 3)/2 parameters. This substantial enlargement over the conventional linear boundaries involves a higher computational cost due to the increased combinatorial number of resulting theoretical QSAR/QSPR models. To face this issue, novel genetic-algorithm-based software, MGZ (multigenetic zooming), was developed and used for both variable selection and model building. To speed up the entire process of domain searching, MGZ was supported with multiple independent evolving populations and genetic storms to further QSAR/QSPR analyses. In addition, a novel fitness function was developed to score models on the basis of their inner predictive capability, assessed on the training set, structure complexity, and presence of nonlinear terms. The models were further validated by monitoring model redundancy and performing intensive randomization runs. The Selwood data set was used as a reference set to derive QSAR models. Furthermore, a QSPR study was conducted on the solubility data set of a large array of organic compounds. The results reported in the present paper demonstrate that our approach is successful in finding linear models, which are at least as good as the models previously derived using standard statistical approaches, and in deriving new nonlinear models with good statistical figures.