Grid potential analysis,virtual screening studies and ADME/T profiling on N‐arylsulfonylindoles as anti‐HIV‐1 agents

A grid potential analysis employing a novel approach of 3D quantitative structure–activity relationships (QSAR) as AutoGPA module in MOE2009.10 was performed on a dataset of 42 compounds of N‐arylsulfonylindoles as anti‐HIV‐1 agents. The uniqueness of AutoGPA module is that it automatically builds the 3D‐QSAR model on the pharmacophore‐based molecular alignment. The AutoGPA‐based 3D‐QSAR model obtained in the present study gave the cross‐validated Q² value of 0.588, r²_pred value of 0.701, r²_m statistics of 0.732 and Fisher value of 94.264. The results of 3D‐QSAR analysis indicated that hydrophobic groups at R₁ and R₂ positions and electron releasing groups at R₃ position are favourable for good activity. To find similar analogues, virtual screening on ZINC database was carried out using generated AutoGPA‐based 3D‐QSAR model and showed good prediction. In addition to those mentioned earlier, in‐silico ADME absorption, distribution, metabolism and excretion profiling and toxicity risk assessment test was performed, and results showed that majority of compounds from current dataset and newly virtually screened hits generated were within their standard limit. Copyright © 2013 John Wiley & Sons, Ltd.     

QSAR of α-campholenic derivatives with sandalwood odor, and molecular design

Abstract  

Quantitative structure–activity relationship (QSAR) studies have been carried out in a series of α-campholenic derivatives with sandalwood odor based on quantum-chemical data derived by use of the Hartree–Fock (HF) method. To build QSAR models, a multiple linear regression method was used. The models obtained have good predictive ability and are of high statistical significance, with good correlation coefficients, and p values less than 0.05. The models contribute also to identification of important quantum-chemical aspects of the sandalwood odor. On the basis of the QSAR models built, several new sandalwood compounds were designed and the best candidate for experimental synthesis was suggested.     

On a new semi‐empirical electrotopological index for QSRR models

A new semi‐empirical electrotopological index, I_SET, for quantitative structure–retention relationships (QSRR) models was developed based on the refinement of the previously published semi‐empirical topological index, I_ET. We demonstrate that the values of C_i fragments that were firstly attributed from the experimental chromatographic retention and theoretical deductions have an excellent relationship with the net atomic charge of the carbon atoms. Thus, the values attributed to the vertices in the hydrogen‐suppressed graph of carbon atoms (C_i) are calculated from the correlation of the net atomic charge in each carbon atom, which is obtained from quantum chemical semi‐empirical calculations, and the C_i fragments for primary, secondary, tertiary and quaternary carbon atoms (1.0, 0.9, 0.8 and 0.7, respectively) obtained from the experimental values. This shows that I_ET encoded this quantum physical reality and that it is possible to calculate a new I_SET (the semi‐empirical electrotopological index) through the net atomic charge values obtained from a Mulliken population analysis using the semi‐empirical AM1 method and their correlation with the values attributed to the different types of carbon atoms. This demonstrates that the I_SET encodes information on the charge distribution of the solute on which dispersive and electrostatic interactions between the solute (alkanes and alkenes) and the stationary phase strongly depend. Thus, this new method can be considered as an initial step towards forthcoming QSRR/QSAR studies. Copyright © 2008 John Wiley & Sons, Ltd.     

Comparative molecular field analysis of non-steroidal aromatase inhibitors related to fadrozole

Summary A series of non-steroidal inhibitors of aromatase, structurally related to fadrozole (2), was investigated with the aim of developing a 3D QSAR model using the Comparative Molecular Field Analysis (CoMFA) technique. The alignment of the molecules was performed following two approaches (atom-by-atom and field fit), both starting from an initial hypothesis of superimposition of fadrozole to a steroidal inhibitor (3). From a number of CoMFA models built with different characteristics, one was recognized as the most statistically relevant; this one is discussed in detail. The features of the 3D QSAR model are consistent with those of other 3D and QSAR models of aromatase and its inhibitors.     

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.     

Conceptual DFT properties‐based 3D QSAR: Analysis of inhibitors of the nicotine metabolizing CYP2A6 enzyme

Structure‐activity relationships of 46 P450 2A6 inhibitors were analyzed using the 3D‐QSAR methodology. The analysis was carried out to confront the use of traditional steric and electrostatic fields with that of a number of fields reflecting conceptual DFT properties: electron density, HOMO, LUMO, and Fukui f⁻ function as 3D fields. The most predictive models were obtained by combining the information of the electron density with the Fukui f⁻ function (r² = 0.82, q² = 0.72), yielding a statistically significant and predictive model. The generated model was able to predict the inhibition potencies of an external test set of five chemicals. The result of the analysis indicates that conceptual DFT‐based molecular fields can be useful as 3D QSAR molecular interaction fields. © 2008 Wiley Periodicals, Inc. J Comput Chem 2009     

In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation

ABSTRACT

Azo dyes are a group of chemical moieties joined by azo (-N=N-) group with potential usefulness in different industrial applications. But these dyes are not devoid of hazardous consequence because of poor affinity for the fibre and discharge into the water stream. The chemical aspects of 72 azo dyes towards cellulose fibre in terms of their affinity by QSPR have been explored in the present work. We have employed two approaches, namely balance of correlation without IIC (TF₁) and balance of correlation with IIC (TF₂), to generate 16 QSAR models from 8 splits. The determination coefficient of calibration and validation set was found higher when the QSPR models were developed using the index of ideality correlation (IIC) parameter (TF₂). The model developed with TF₂ for split 3 was considered as a prominent model because the determination coefficient of the validation set was maximum (r ² = 0.9468). The applicability domain (AD) was also analysed based on ‘statistical defect’, d(A) for a SMILES attribute. The mechanistic interpretation was done by identifying the SMILES attributes responsible for the promoter of endpoint increase and promoter of endpoint decrease. These SMILES attributes were applied to design 15 new dyes with higher affinity for cellulose fibre.