Application of Substituent Electronic Descriptors QSAR Model of 2-amino-6-arylsulfonylbenzonitriles as HIV-1 Reverse Transcriptase Inhibitors based on the MOLMAP Approach (original) (raw)
Related papers
Virology: Research and Reviews, 2017
The aim of this study is to find the relationship between HIV-1 activity and chemical structure for 2-Pyridinone derivatives by using the Electron-Topological Method (ETM). Data for ETM were obtained quantum mechanical calculations. Quantum chemical calculations were performed after the conformational analysis. By using the data obtained from quantum chemical calculation results ETM were perfomed and pharmacophere and anti-pharmacophere fragments for the HIV-1specific Reverse Transcriptase inhibitors were explained. Conformational analysis and quantum-chemical calculations of 2-pyridinone derivatives were carried out by using B3LYP method with basis set of the 6-311G(d,p) in order to determine molecular properties. The descriptors of HOMO, LUMO, HOMO-LUMO energy gap, chemical hardness, chemical softness, electro-negativity, chemical potential, dipole moment etc. were calculated and tabulated in order to employed in statistical analyses that are Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs). By doing so, the linear and non-linear sections of data structure are investigated and their corresponding descriptors having impact on dependent variable has been found. We see from the fragment properties atoms found in benzoxazole groups give rise to activity of the molecules, and atoms in the naphthyl groups causes breaking the activity.
Bioorganic & Medicinal Chemistry Letters, 2004
In pursuit of better anti-HIV drugs, a quantitative structure–activity relationship analysis using a novel set of 2D descriptors was performed on a series of HIV-1 reverse transcriptase inhibitory benzoxazinones. The QSAR models derived from the above mentioned descriptors were found to be statistically significant and exhibited superior predictive power. The results of the study justify the application of the descriptors for exploring the binding mode of the benzoxazinones to the enzyme.A novel series of HIV-1 reverse transcriptase inhibitors was subjected to quantitative structure–activity relationship analysis (QSAR) employing a novel set of P_VSA descriptors.
QSAR & Combinatorial Science, 2004
Quantitative structure-activity relationships (QSAR); Acquired immuno deficiency syndrome (AIDS); Human immunodeficiency virus (HIV); Linear free energy related (LFER); Reverse transcriptase (RT); Nucleoside reverse transcriptase inhibitors (NRTIs); Non-nucleoside reverse transcriptase inhibitors (NNRTIs), Austin model 1 (AM1); Molar refractivity (MR) Classical QSAR Modeling of HIV-1 Reverse Transcriptase Inhibitor 2-Amino-6-arylsulfonylbenzonitriles and ...
Journal of the Serbian Chemical Society, 2019
Acquired immunodeficiency syndrome (AIDS) is a significant human health threat around the world and therefore, the study of anti-HIV drug design has become an important task for today's society. In this paper, a three-dimensional quantitative structure-activity relationships study (3D-QSAR) was conducted on 72 HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) using Topomer comparative molecular field analysis (Topomer CoMFA). The multiple correlation coefficients of fitting, cross-validation, and external validation were 0.899, 0.788 and 0.942, respectively. The results indicated that the obtained model had both a favorable estimation stability and a good prediction capability. Topomer Search was used to search appropriate R groups from the ZINC database, Thereby, 14 new compounds were designed, and 12 of the new compounds were predicted to be more active than the template molecule. These results strongly suggest that the Topomer search was effective in screening and could be a useful guide in the design of new HIV-1 drugs. The ligands of the template molecule and the new designed compounds were used for molecular docking to study the interaction of these compounds with the protein receptor. The results showed that the ligands would generally form hydrogen-bonding interactions with the residues Ala28, Asp29, Gly49 and Ile50 of the protein receptor, thereby providing additional insights for the designing of even more effective drugs.
Journal of Chemical Sciences, 2007
Lipophilicity or hydrophobicity is a crucial physico-chemical property of an oral drug compound. In the present study, we have analysed the structural parameters responsible for enhancing the lipophilicity expressed in terms of Octanol-Water partition coefficient, log P, of 2-amino-6-arylsulfonylbenzonitrile (AASBN) derivatives used as NNRTIs in AIDS therapy. Connectivity based Randic (χ) and Balaban (J) and atomistic Kier-Hall electrotopological state (E-state) indices have been used to develop Quantitative Structure-Property Relationship (QSPR) and to predict the effect of substitution on the log P. Model has been developed using multiple linear regression analysis (MLR) for the training set (67 compounds) and the model was tested on a test set (7 compounds). Significant results were obtained for the training set (R 2 = 0·948, R adj2 = 0·939, SE = 0·177, F-ratio = 101·22). The results of the test set too implicated a good fit (R 2 = 0·941, R adj2 = 0·929, SE = 0·157, F-ratio = 80·05). Among the two connectivity based topological indices; Randic (χ) index showed better predictive ability than the Balaban (J) index. Kier-Hall E-state indices indicated that among the functional groups, methyl, bromo, chloro groups on ring A, with their positive coefficients enhanced the lipophilicity. Amino, cyano group on ring B and the bridging S, SO, SO2 with their negative coefficients showed an adverse effect on the lipophilicity parameter. Thus, Kier-Hall E-state indices along with topological indices could be well applied for deriving QSPR models and analysing substitution effects of various functional groups. The training set, correlation matrix and observed and experimental log P values are available as supplementary material for this article.
Structure-activity correlation study of HIV-1 inhibitors: Electronic and molecular parameters
1996
SummaryQuantitative structure-activity relationships (QSARs) for 40 HIV-1 inhibitors, 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine and its derivatives, were studied. Fully optimized geometries, based on the semiempirical AM1 method, were used to calculate electronic and molecular properties of all compounds. In order to examine the relation between biological activities and structural properties, multiple linear regression models were employed. A suitable QSAR model was obtained, showing not only statistical significance, but also predictive ability. The significant molecular descriptors used were atomic charges of two substituted carbon atoms in the thymine ring, hydration energies and molar refractivities of the molecules. These descriptors allowed a physical explanation of electronic and molecular properties contributing to HIV-1 inhibitory potency.
A molecular-field-based similarity study of non-nucleoside HIV1 reverse transcriptase inhibitors
Journal of Computer-aided Molecular Design, 1999
This article describes a molecular-field-based similarity method for aligning molecules by matching their steric and electrostatic fields and an application of the method to the alignment of three structurally diverse non-nucleoside HIV-1 reverse transcriptase inhibitors. A brief description of the method, as implemented in the program MIMIC, is presented, including a discussion of pairwise and multi-molecule similarity-based matching. The application provides an example that illustrates how relative binding orientations of molecules can be determined in the absence of detailed structural information on their target protein. In the particular system studied here, availability of the X-ray crystal structures of the respective ligand-protein complexes provides a means for constructing an 'experimental model' of the relative binding orientations of the three inhibitors. The experimental model is derived by using MIMIC to align the steric fields of the three protein P66 subunit main chains, producing an overlay with a 1.41 Å average rms distance between the corresponding C α 's in the three chains. The inter-chain residue similarities for the backbone structures show that the main-chain conformations are conserved in the region of the inhibitor-binding site, with the major deviations located primarily in the 'finger' and RNase H regions. The resulting inhibitor structure overlay provides an experimental-based model that can be used to evaluate the quality of the direct a priori inhibitor alignment obtained using MIMIC. It is found that the 'best' pairwise alignments do not always correspond to the experimental model alignments. Therefore, simply combining the best pairwise alignments will not necessarily produce the optimal multi-molecule alignment. However, the best simultaneous three-molecule alignment was found to reproduce the experimental inhibitor alignment model. A pairwise consistency index has been derived which gauges the quality of combining the pairwise alignments and aids in efficiently forming the optimal multi-molecule alignment analysis. Two post-alignment procedures are described that provide information on feature-based and field-based pharmacophoric patterns. The former corresponds to traditional pharmacophore models and is derived from the contribution of individual atoms to the total similarity. The latter is based on molecular regions rather than atoms and is constructed by computing the percent contribution to the similarity of individual points in a regular lattice surrounding the molecules, which when contoured and colored visually depict regions of highly conserved similarity. A discussion of how the information provided by each of the procedures is useful in drug design is also presented.
International Journal of Molecular Sciences, 2011
The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom's polynomials allow either uni-or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the "optimal molecular structural domains" and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1
Drug design, development and therapy, 2016
A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations. To the best of our knowledge; this is the first attempt in the literature using such an approach; previous approaches have relied on the docking score to generate energy-based pharmacophore models. However, docking scores are reportedly unreliable. Thus, we present a model for a per-residue energy decomposition, constructed from MD simulation ensembles generating a more trustworthy pharmacophore model, which can be applied in drug discovery wo...
In-silico Discovery and Simulated Selection of Multi-target Anti-HIV-1 Inhibitors
International Research Journal of Pure and Applied Chemistry, 2016
The multi-target quantitative structure-activity relationship (mt-QSAR) study of human immunodeficiency virus (HIV-1) inhibitors was addressed by applying a modest, hitherto active linear regression model based on the Genetic function approximation. QSAR studies were performed on two datasets of HIV-1 inhibitors targeted on integrase and reverse transcriptase, respectively. By using the genetic function approximation method, the collaboration among different set of inhibitors was exploited and an efficient multi-target QSAR modeling for HIV-1 inhibitors was obtained. The predictive quality of the mt-QSAR models was tested for an external set of 30 compounds, randomly chosen out of 150 compounds. The linear regression model based on the Genetic function approximation with eight selected descriptors was obtained. The accuracy of the Original Research Article Edache et al.; IRJPAC, 11(1): 1-15, 2016; Article no.IRJPAC.22863 2 proposed model is illustrated using the following evaluation...