Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log?P data from a widely used steroid benchmark data set.     

Development of an ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system,March 2009 version

The KAshinhou Tool for Ecotoxicity (KATE) system, including ecotoxicity quantitative structure–activity relationship (QSAR) models, was developed by the Japanese National Institute for Environmental Studies (NIES) using the database of aquatic toxicity results gathered by the Japanese Ministry of the Environment and the US EPA fathead minnow database. In this system chemicals can be entered according to their one-dimensional structures and classified by substructure. The QSAR equations for predicting the toxicity of a chemical compound assume a linear correlation between its log P value and its aquatic toxicity. KATE uses a structural domain called C-judgement, defined by the substructures of specified functional groups in the QSAR models. Internal validation by the leave-one-out method confirms that the QSAR equations, with r ² > 0.7, RMSE ≤ 0.5, and n > 5, give acceptable q ² values. Such external validation indicates that a group of chemicals with an in-domain of KATE C-judgements exhibits a lower root mean square error (RMSE). These findings demonstrate that the KATE system has the potential to enable chemicals to be categorised as potential hazards.     

Nonlinear Dependence of Fish Bioconcentration on n-Octanol/Water Partition Coefficient

Abstract

The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n?octanol/water partition coefficients breaks down for very hydrophobic compounds. The use of parabolic and bilinear models allows this problem to be overcome. The QSAR equation log BCF = 0.910 log P - 1.975 log (6.8 10^?7 P + 1) - 0.786 (n = 154; r = 0.950; s = 0.347; F = 463.51) was found to be a good predictor of bioconcentration in fish.     

DFT‐based QSAR study and molecular design of AHMA derivatives as potent anticancer agents

A quantitative structure–activity relationship (QSAR) of 3‐(9‐acridinylamino)‐5‐hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy (E_NL) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges (Q_FR) of the first atom of the substituent R, as well as the steric parameter (MR₂) of subsituent R₂ are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by “leave‐one‐out” (LOO) cross‐validation coefficient (q) was suggested and successfully used. The fitting correlation coefficient (R²) and the “LOO” cross‐validation coefficient (q²) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007     

脂质体电动色谱用于预测皮肤渗透性的定量保留活性关系的建立

脂质体电动色谱 （Liposome electrokinetic chromatography,LEKC）是一种简单快速的评价药物与生物膜相互作用的方法。本文建立了脂质体电动色谱作为高通量筛选皮肤渗透性的体外分析方法。将脂质体电动色谱中保留因子的对数值（log k）作为自变量建立了定量保留活性关系式。采用SPSS分析软件对于16种结构不同的化合物进行分析,结果表明log k与皮肤渗透性常数线性相关性良好( R²=0.886)。采用交互验证评价了该模型的预测能力。在定量保留活性关系中的一个变量和传统定量构效关系中的三个变量可解释的能力( R² =0.704)相似。文中建立的定量保留活性关系模型对于新化合物早期的筛选可提供一种有效快捷的方法。     

Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model

Quantitative structure–activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham’s linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham’s LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q² _LOO statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r² = 0.70) improved both the model fit and the predictive power when compared with the simple model (r² = 0.21).     

How medicinal chemists learned about log <Emphasis Type="Italic">P</Emphasis>

Although log P is now recognized to be a key factor that determines the bioactivity of a molecule, the focus of medicinal chemists on hydrophobicity and log P started with the quantitative structure–activity relationships (QSAR) publications of Hansch and Fujita. Their original publication represents a dramatic change of focus to incorporate consideration of log P after a decade of work unsuccessfully attempting to use the Hammett equation to explain the structure-activity relationships of plant growth regulators. QSAR allows one to explore the quantitative relationship between log P and biological activity even when other factors also influence potency. In particular, Hansch’s publications of thousands of QSAR equations demonstrate that a relationship of biological activity with log P is indeed a general phenomenon. Hansch’s group also provided data and tools that enable others to explore the relationship between log P and the biological activity of compounds of interest.     

Determination of Retention Factors of s-Triazines Homologous Series in Water Using a Numerical Method Basing on Ościk's Equation

The retention factors in pure water for a homologous series of s-triazines were calculated by a numerical method basing on Ościk's equation and were correlated with log k _w values obtained by linear and parabolic extrapolation. Chromatographic data (log k _w ) were compared with the software-calculated partition coefficients in the n-octanol/water system (Alog P, IAlog P, clog P, log P _Kowin , xlog P, log P _ACD and log P _Chem.Off.) as alternative hydrophobicity indices. The effect of organic modifier (methanol and acetonitrile) and its concentration in the mobile phase used for log k _w evaluation were investigated. Very good linear correlations were found between log k _w values calculated by the numerical method and log P _ACD , log P _Chem.Off . and clog P values, independent of organic modifier type.