Introducing Catastrophe-QSAR. Application on Modeling Molecular Mechanisms of Pyridinone Derivative-Type HIV Non-Nucleoside Reverse Transcriptase Inhibitors (original) (raw)
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Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method
Central European Journal of Chemistry, 2013
The CORAL software (http://www.insilico.eu/coral/) has been examined as a tool for modeling anti-HIV-1 activity by quantitative structure — activity relationships (QSAR) for three different sets: (i) TIBO derivatives (n=82) (ii) anti-HIV-1 activity of 2-amino-6-arylsulfonylbenzonitriles and their congeners (n=64), and (iii) the measured binding affinity for fullerene-based HIV-1 PR inhibitors (n=48). A new global invariant ATOMPAIR of the molecular structure which can be calculated with the simplified molecular input line entry system (SMILES) was studied. The ATOMPAIR is an indicator of the joint presence of pairs of chemical elements (F, Cl, Br, N, O, S, and P) and three types of bonds (double covalent bond, triple covalent bond, and stereo chemical bond). Six random splits into sub-training, calibration, and test set were examined for each set. For the three aforementioned sets, the use of ATOMPAIR in the modeling process improves the predictive potential of the models for six ra...
Journal of Enzyme Inhibition and Medicinal Chemistry, 2008
Quantitative structure-activity relationship (QSAR) studies have been carried out on indolyl aryl sulfones, a class of novel HIV-1 non-nucleoside reverse transcriptase inhibitors, using physicochemical, topological and structural parameters along with appropriate indicator variables. The statistical tools used were linear methods (e.g., stepwise regression analysis, partial least squares (PLS), factor analysis followed by multiple regression (FA-MLR), genetic function approximation combined with multiple linear regression (GFA-MLR) and GFA followed by PLS or G/PLS and nonlinear method (artificial neural network or ANN). In case of physicochemical parameters, GFA-MLR generated the best Equation (n ¼ 97, R 2 ¼ 0.862, Q 2 ¼ 0.821). Using topological parameters, the best Equation (based on leave-one-out Q 2) was obtained with stepwise regression technique (n ¼ 97, R 2 ¼ 0.867, Q 2 ¼ 0.811). When topological and physicochemical parameters were used in combination, statistical quality increased to a great extent (n ¼ 97, R 2 ¼ 0.891, Q 2 ¼ 0.849 from stepwise regression). Furthermore, the whole dataset had been divided into test (25% of whole dataset) and training (remaining 75%) sets. Models were developed based on the training set and predictive potential of such models was checked from the test set. The selection of the training set was based on K-means clustering of the standardized descriptors (topological and physicochemical). In this case also the best results were obtained with stepwise regression (n ¼ 72, R 2 ¼ 0.906, Q 2 ¼ 0.853) but external predictive capacity of this model (R 2 pred ¼ 0:738) was inferior to the model developed from GFA-MLR technique (R 2 ¼ 0.883, Q 2 ¼ 0.823, R 2 pred ¼ 0:760). However, the squared regression coefficient between observed activity and predicted activity values of the test set compounds for the best linear model, i.e., GFA-MLR (r 2 ¼ 0.736) was lower in comparison to the best nonlinear model developed using artificial neural network (r 2 ¼ 0.781). Thus, based on external validation, the ANN models were superior to the linear models. The predictive potential of the best linear Equation (stepwise regression model) was superior to that of the previously published CoMFA (Q 2 ¼ 0.81, SDEP Test ¼ 0.89) on the same data set (Ragno R. et al., J Med Chem 2006, 49, 3172-3184). Furthermore, the physicochemical parameter based models also supported the previous observations based on docking (Ragno R. et al.,
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.
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.
Arabian Journal of Chemistry, 2016
In this work, quantitative structure-activity relationship (QSAR) study has been done on tricyclic phthalimide analogues acting as HIV-1 integrase inhibitors. Forty compounds were used in this study. Genetic algorithm (GA), artificial neural network (ANN) and multiple linear regressions (MLR) were utilized to construct the non-linear and linear QSAR models. It revealed that the GA-ANN model was much better than other models. For this purpose, ab initio geometry optimization performed at B3LYP level with a known basis set 6-31G (d). Hyperchem, ChemOffice and Gaussian 98W softwares were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. To include some of the correlation energy, the calculation was done with the density functional theory (DFT) with the same basis set and Becke's three parameter hybrid functional using the LYP correlation functional (B3LYP/6-31G (d)). For the calculations in solution phase, the polarized continuum model (PCM) was used and also included optimizations at gas-phase B3LYP/6-31G (d) level for comparison. In the aqueous phase, the root-mean-square errors of the training set and the test set for GA-ANN model using jack-knife method, were 0.1409, 0.1804, respectively. In the gas phase, the
The antiviral drugs that inhibit human immunodeficiency virus (HIV) entry to the target cells are already in different phases of clinical trials. They prevent viral entry and have a highly specific mechanism of action with a low toxicity profile. Few QSAR studies have been performed on this group of inhibitors. This study was performed to develop a quantitative structure-activity relationship (QSAR) model of the biological activity of indole glyoxamide derivatives as inhibitors of the interaction between HIV glycoprotein gp120 and host cell CD4 receptors. Forty different indole glyoxamide derivatives were selected as a sample set and geometrically optimized using Gaussian 98W. Different combinations of multiple linear regression (MLR), genetic algorithms (GA) and artificial neural networks (ANN) were then utilized to construct the QSAR models. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear log (1/EC 50 ) prediction. The results that were obtained using GA-ANN were compared with MLR-MLR and MLR-ANN models. A high predictive ability was observed for the MLR, MLR-ANN and GA-ANN models, with root mean sum square errors (RMSE) of 0.99, 0.91 and 0.67, respectively (N = 40). In summary, machine learning methods were highly effective in designing QSAR models when compared to statistical method.
IJERT-Modeling of Activity of Cyclic Urea HIV-1 Protease Inhibitors using QSAR
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/modeling-of-activity-of-cyclic-urea-hiv-1-protease-inhibitors-using-qsar https://www.ijert.org/research/modeling-of-activity-of-cyclic-urea-hiv-1-protease-inhibitors-using-qsar-IJERTV10IS020065.pdf In this present work a Bayesian-Regularized artificial neural networks technique (BRNNs) was used of cyclic-urea derivatives, inhibiting HIV-1 protease. As a preliminary step, linear dependences were established by means a of multiple linear regression (MLR) approaches, selecting the relevant descriptors .The quality of the models was evaluated by means of cross-validation procedure and the best results correspond to nonlinear ones. The results are along the same lines as those of our previous studies on cyclic derivatives and indicate the importance of the hydrophobic parameter in and steric modelling the QSAR for cyclic urea derivativesis.
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.
Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives
European Journal of Medicinal Chemistry, 2009
Comparative quantitative structure-activity relationship (QSAR) studies have been carried out on tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives as reverse transcriptase inhibitors (n ¼ 70) using topological, structural, physicochemical, electronic and spatial descriptors. The data set was divided into training and test sets using a cluster-based method. Linear models were developed using multiple regression (with stepwise regression, factor analysis and genetic function approximation (GFA) as variable selection tools) and partial least squares (PLS) and combination of factor analysis and partial least squares (FA-PLS). Genetic function approximation (spline) and artificial neural networks (ANN) were used for the development of non-linear models. Using topological and structural descriptors, the best equation was obtained from GFA (spline) based on internal validation (Q 2 ¼ 0.737), but the model with the best external validation characteristics was obtained with FA-PLS (R pred 2 ¼ 0.707). When structural, physicochemical, electronic and spatial descriptors were used, the best Q 2 (0.740) value was obtained from GFA (spline) whereas PLS provided the best R pred 2 (0.784) value. When all descriptors were used in combination, the best R pred 2 (0.760) value and the best Q 2 (0.800) value were obtained from ANN and GFA (spline), respectively. The majority of the models satisfied the criteria of external validation recommended by Golbraikh and Tropsha (2002) and the criteria of modified r 2 (r m 2) values of the test set for external validation as suggested by Roy and Roy (2008). In order to further validate selected models, an external set of 10 TIBO derivatives, which fall within the applicability domain of the models and are not shared with the compounds of the present data set, was taken from a different source, and reverse transcriptase inhibitory activity of these compounds was predicted. Acceptable values of squared correlation coefficients between the observed and predicted values of the external set compounds were obtained from the selected models suggesting true predictive potential of the models.
Asian Journal of Applied Chemistry Research, 2018
The HIV-1 reverse transcriptase (RT) is a major target for drug development. Inhibition of this enzyme has been one of the primary therapeutic strategies in suppressing the replication of HIV-1. A series of 2-amino-6-arylsulfonylbenzonitrile derivatives were subjected to quantitative structure-activity relationship (QSAR) analysis. Very recently, we proposed the use of substituent electronic descriptors (SED) instead of the electronic descriptors of whole molecules as new and expedite source of electronic descriptors. In this study, we used SED parameters in QSAR modeling of anti HIV-1 activity of 6-arylsulfonylbenzonitrile derivatives. In SED methodology produces a vector of electronic descriptors for each substituent and thus a matrix of SED is generated for each molecule. Consequently, a three-dimensional array is obtained by staking the data matrices of different molecules beside each other. As a novel multiway data analysis method, molecular maps of atom-level properties (MOLMA...