Prediction of anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amines: A QSAR study

A QSAR study on a series of pyrimidinyl and triazinyl amines was performed to explore the physico-chemical parameters responsible for their anti-HIV activity and cytotoxicity. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was carried out to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR models showed correlation coefficient R of 0.914 and 0.901, and cross-validated squared correlation coefficient Q ² of 0.685 and 0.691 for anti-HIV activity and cytotoxicity, respectively. The developed significant QSAR model indicates that hydrophobicity of the whole molecule plays an important role in the anti-HIV activity and cytotoxicity of pyrimidinyl and triazinyl amine derivatives. When hydrophobicity is increased, anti-HIV activity of the present series of compounds is decreased leading to high cytotoxicity.     

An Analysis of the Relative Occurrence of N-Demethylation and N-Oxidation in Xenobiotic Metabolism Using Structure-Reactivity Maps

Abstract

The relative occurrence of N?demethylation and N?oxidation in a structurally diverse set of N?methyl tertiary amines was examined using structure-reactivity maps. A structure-reactivity map of the data indicated important structural features useful for predicting the relative occurrence of these reactions. A steric index which describes the degree of steric hinderance at the N?methyl reaction site is defined. A QSAR between the relative occurrence of N?demethylation and N?oxidation of N?methyl tertiary amines and the steric index was developed.     

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.     

Am指数的扩展与手性化合物的构效关系研究

构效关系研究中的分子拓扑指数(Am)通常仅代表一个化合物的拓扑特征,所以预测手性化合物活性的能力较差.我们对Am指数进行了扩展,得到eAm指数,并将其应用于手性分子的结构-活性相关研究.结果表明,由手性拓扑指数得到的QSAR模型比传统的拓扑指数有更好的统计和预测手性化合物活性的能力.     

Rational selection of training and test sets for the development of validated QSAR models

Quantitative Structure–Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R² (q²) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q ² for the training set and accuracy of prediction (R ²) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.     

Synthesis, antimicrobial evaluation and QSAR studies of 3-ethoxy-4-hydroxybenzylidene/4-nitrobenzylidene hydrazides

A series of 3-ethoxy-4-hydroxybenzylidene/4-nitrobenzylidene hydrazides (1–20) was synthesized and tested for in vitro antimicrobial activity. The results of antimicrobial studies indicated that the compounds having dinitro, methoxy, hydroxy and nitro substituents on phenyl ring of the aromatic acids were most active ones. The QSAR investigation indicated the importance of the topological parameter, third order molecular connectivity index (³χ) in describing the antimicrobial activity of synthesized hydrazides.