Test Set Compounds Research Articles

Ligand-based pharmacophore filtering, atom based 3D-QSAR, virtual screening and ADME studies for the discovery of potential ck2 inhibitors

In the era of druggable genome, the assessment of the numerous molecular targets represents remarkable therapeutic opportunities in the pharmaceutical and chemical biology, simultaneously understanding the properties required for the small molecules to emerge as good drug candidate. Incorporation of readily amenable biological properties and pharmaceutical modulation is the core key for the small molecule driven target studies in cancer related disease, especially in the case of inhibitory type mechanisms. Among huge protein targets, CK2 which is a protein serine/threonine kinase, also called the “Predominant monitor” of the cell plays comprehensive role in the various cellular machinery pertaining to cell growth and cell death. Due to its ubiquitous nature and its activity to block its activity by small molecules resulting in favorably targeting prostate cancer, CK2 was identified to be the distinct element of our study. In this study, we invested rapid computational techniques to uncover the new CK2 inhibitors with promising pharmaceutical traits and advantages when matched with existing drugs. Initially, pharmacophore modeling and atom-based 3Dimensional-Quantitative structure activity relationship of 45 known CK2 inhibitors resulted in over few hundred hypotheses. The most excellent five point pharmacophore model (AAHHR) with two hydrogen bond acceptor (A), two hydrophobic groups (H), and one aromatic ring (R) was built. 3D-QSAR studies of the finest model yielded correlation co-efficient, R2 (0.9728) and Q2 (0.7965) for training and test set compounds respectively. Our effort of externally validating the generated QSAR model was quite momentous and encouraging with rm2 = 0.682, rcv2 = 0.779, k = 1.027 and r02 = 0.817 values points out the profoundness in predicting preeminence of model. The robust model was further employed as 3D query for virtual screening against ZINC database. The lead molecules were selected based on the fitness score, and then the lead molecules subsequently taken to molecular docking studies using Glide. Finally we identified six potential lead molecules were further subjected into ADME properties prediction by engaging Qikprop module. The ADME properties of six lead molecules ZINC15955420, Zinc13412605, Zinc40763681, Zinc40763677, Zinc26178676 and Zinc01536721 are under satisfactory range with desired ADME properties. On the whole, we believe our design of the new CK2 inhibitor serves as the approachable end resultant hits the can be researched further for clinical trials evaluation in prostate cancer and emphasize on choosing CK2 as the target that holds true protential for the genesis and proliferation of anti-CK2 agents to address prostate cancer therapeutics.

Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods

HIV-1 protease inhibitors (PIs) make a vital contribution on highly active antiretroviral therapy (HAART) of human immunodeficiency virus (HIV). In this study, 14 quantitative structure-activity relationship (QSAR) models on 1238 PIs were built by four machine learning methods, including multiple linear regression (MLR), support vector machine (SVM), random forest (RF) and deep neural networks (DNlN). For the best model Model2G constructed by DNN algorithm, the coefficient of determination (R2) of 0.88 and 0.79, the root mean squared error (RMSE) of 0.39 and 0.51 were obtained on training set and test set, respectively. For model Model2G, the applicability domain threshold (ADT) of 1.765 was obtained for training set, a compound that has a similarity distance (d) less than the ADT is considered to be inside the applicability domain, could be predicted accurately, and thus 65.37% compounds in test set performed reliable. In addition, the 1238 PIs were manually divided into eight subsets containing different scaffolds. It was found that hydroxylamine derivatives and seven-member cyclic urea derivatives showed highly inhibitory activity comparing with other subsets. We also built QSAR models with SVM, RF and DNN methods on two subsets of 299 hydroxylamine derivatives inhibitors (Dataset2) and 377 seven-member cyclic urea derivatives inhibitors (Dataset3). For the best model Model3A on Dataset2, R2 of 0.71 and RMSE of 0.53 were obtained for test set. For the best model Model4B on Dataset3, R2 of 0.82 and RMSE of 0.51 were obtained for test set. At last, we analyzed the descriptors which make significant contributions on the bioactivity of inhibitors among these two subsets. It was found that highly active inhibitors of seven-member cyclic urea derivatives usually contained several aromatic nitrogen heterocyclic ring substituents such as the inidazole and the pyrazole. The oxazolidinone group and sulfanilamide mainly appeared in highly active inhibitors of hydroxylamine derivatives. These observations may be utilized further in designing promising HIV-1 protease inhibitors.

In silico QSAR and molecular docking studies of sulfur containing shikonin oxime derivatives as anti-cancer agent for colon cancer

ObjectivesThis study is aimed at generating a statistically valid model using molecular descriptors and physicochemical parameters on sulfur-containing shikonin oxime derivatives and to have better understanding of binding affinity of these compounds as well as their various interactions with the target receptor so as to pave way for the designing of highly potent anti-colon cancer drug. Materials and methodsQuantitative structure–activity relationship and molecular docking studies were carried out on 40 dataset of sulfur containing shikonin oxime derivatives. Density functional theory were employed in optimizing the molecule with B3LYP/6-31G* as basis set, while descriptor selection and generation of multiple linear regression model that correlates the structural feature of the compounds to their experimental activities was done by employing Genetic Function Algorithm (GFA). The softwares that was used in this study include Chemdraw, Spartan software, Padel, Material studio, Pyrex, Vina wizard, Autoduck, as well as Discovery studio. ResultsAmong the models generated, the one with best statistical significance was selected and it has R2 value of 0.92, adjusted R2 value of 0.89, and leave one out (LOO) cross validation coefficient (Q2) value of 0.85. To confirm the predictive capacity of the selected model, external validation was carried out using the test set compounds and it has the R2ext value of 0.88. Molecular docking study revealed that all the compounds have favorable binding affinity to the target receptor, compound number 23 has the highest binding affinity of −8.3 kcal/mol and favorable number of various interactions. ConclusionBased on the parameters of internal and external validation, the robustness, stability and predictive ability of the selected model was verified, compounds number 23 with highest binding affinity and favorable number of various interactions can be used as lead compound in the feature design of highly potent anti-colon cancer drug.

Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II

Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.

Predicting estrogen receptor binding of chemicals using a suite of in silico methods – Complementary approaches of (Q)SAR, molecular docking and molecular dynamics

With the aim of obtaining reliable estimates of Estrogen Receptor (ER) binding for diverse classes of compounds, a weight of evidence approach using estimates from a suite of in silico models was assessed. The predictivity of a simple Majority Consensus of (Q)SAR models was assessed using a test set of compounds with experimental Relative Binding Affinity (RBA) data. Molecular docking was also carried out and the binding energies of these compounds to the ERα receptor were determined. For a few selected compounds, including a known full agonist and antagonist, the intrinsic activity was determined using low-mode molecular dynamics methods. Individual (Q)SAR model predictivity varied, as expected, with some models showing high sensitivity, others higher specificity. However, the Majority Consensus (Q)SAR prediction showed a high accuracy and reasonably balanced sensitivity and specificity. Molecular docking provided quantitative information on strength of binding to the ERα receptor. For the 50 highest binding affinity compounds with positive RBA experimental values, just 5 of them were predicted to be non-binders by the Majority QSAR Consensus. Furthermore, agonist-specific assay experimental values for these 5 compounds were negative, which indicates that they may be ER antagonists. We also showed different scenarios of combining (Q)SAR results with Molecular docking classification of ER binding based on cut-off values of binding energies, providing a rational combined strategy to maximize terms of toxicological interest.

Quantitative Structure-Activity Relationships for Structurally Diverse Chemotypes Having Anti-Trypanosoma cruzi Activity.

Small-molecule compounds that have promising activity against macromolecular targets from Trypanosoma cruzi occasionally fail when tested in whole-cell phenotypic assays. This outcome can be attributed to many factors, including inadequate physicochemical and pharmacokinetic properties. Unsuitable physicochemical profiles usually result in molecules with a poor ability to cross cell membranes. Quantitative structure-activity relationship (QSAR) analysis is a valuable approach to the investigation of how physicochemical characteristics affect biological activity. In this study, artificial neural networks (ANNs) and kernel-based partial least squares regression (KPLS) were developed using anti-T. cruzi activity data for broadly diverse chemotypes. The models exhibited a good predictive ability for the test set compounds, yielding q2 values of 0.81 and 0.84 for the ANN and KPLS models, respectively. The results of this investigation highlighted privileged molecular scaffolds and the optimum physicochemical space associated with high anti-T. cruzi activity, which provided important guidelines for the design of novel trypanocidal agents having drug-like properties.

2D-QSPR Study of Olfactive Thresholds for Pyrazine Derivatives Using DFT and Statistical Methods

In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods.The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

Exploring QSPR modeling for adsorption of hazardous synthetic organic chemicals (SOCs) by SWCNTs

In order to understand the physicochemical properties as well as the mechanisms behind adsorption of hazardous synthetic organic chemicals (SOCs) onto single walled carbon nanotubes (SWCNTs), we have developed partial least squares (PLS)-regression based QSPR models using a diverse set of 40 hazardous SOCs having defined adsorption coefficient (logK). The models were extensively validated using different validation parameters in order to assure the robustness and predictivity of the models. We have also checked the consensus predictivity of all the individual models using “Intelligent consensus predictor” tool for possible enhancement of the quality of predictions for test set compounds. The consensus predictivity of the test set compounds were found to be better than the individual models based on not only the MAE based criteria (MAE(95%) = Good) but also some other validation parameters (Q2F1 = 0.938, Q2F2 = 0.937). The contributing descriptors obtained from the QSPR models suggested that the hazardous SOCs may get adsorbed onto the SWCNTs through hydrophobic interaction as well as hydrogen bonding interactions and electrostatic interaction to the functionally modified SWCNTs. Thus, the developed models may provide knowledge to scientists to increase the efficient application of SWCNTs as a special adsorbent, which may be useful for the management of environmental pollution.

In-silico designing and characterization of binding modes of two novel inhibitors for CB1 receptor against obesity by classical 3D-QSAR approach

Obesity is the fifth primary hazard for mortality in the world; hence different therapeutic targets are explored to overcome this problem. Endocannabinoid is identified as the emerging target for the treatment of obesity as Cannabinoid 1 (CB1) receptor over-activation resulted in abdominal obesity. Potent antagonists or inverse agonists for CB1 receptor are the new strategies to develop anti-obesity drugs. Here, ligand-based 3D-QSAR studies was performed on 100 analogues belonging to a class of 1,2,4-tirazole containing diarylpyrazolylcarboxamide as CB1 receptor antagonists. We developed three CoMFA models using different charge schemes, AM1BCC, Gasteiger-Huckle and MMFF. These models produced almost similar statistical results (q2cv = 0.725, 0.692, 0.719 and r2ncv = 0.929, 0.924, 0.928 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). The said models were validated through 20 external test set compounds which resulted in significant r2pred values (r2pred = 0.747, 0.743 and 0.745 for AM1BCC, Gasteiger-Huckle and MMFF, respectively). Comparatively, AM1BCC model provided slightly better statistics among all three tested charges scheme models, hence AM1BCC model was further utilized to generate CoMSIA models considering different field combinations. The best selected CoMSIA model also produced substantial q2cv = 0.788, r2ncv = 0.916 and r2pred = 0.836 values. Furthermore, two new molecules were designed by modifying the same scaffolds on the basis contour map analysis. The activities of newly designed molecules were predicted through obtained CoMFA model ranked as better than their parent molecules. Moreover, these newly designed compounds were successfully docked on the complex crystal structure of CB1 receptor (PDB ID: 5XRA). The docked conformation of these newly designed inhibitor interacted with Ser173, His178, Lys192, Thr197 and Ser383 mainly by hydrophobic and pi-pi stacking interactions. The obtained results signify the potential of the developed model; suggesting that the models can be useful to test and design potent novel CB1 receptor antagonists or inverse agonists prior to the synthesis.

Chemometric modeling of Daphnia magna toxicity of agrochemicals

Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. In the current work, we have developed quantitative structure-activity relationship (QSAR) models for Daphnia magna toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization for Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual models were used to generate consensus models, with the objective to improve the prediction quality and reduced prediction errors. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. The general observations from the derived models suggest that the toxicity increases with lipophilicity and decreases with polarity. The generated models of different classes of agrochemicals and also for the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

Combined comparative molecular field analysis, comparative molecular similarity indices analysis, molecular docking and molecular dynamics studies of histone deacetylase 6 inhibitors.

Human histone deacetylase isoform 6 (HDAC6) has been shown to have an immense role in cell motility and aggresome formation and is being an attractive selective target for the treatment of multiple tumour types and neurodegenerative conditions. The discovery of selective HDAC6 inhibitors with new chemical functionalities is therefore of utmost interest to researchers. In order to examine the structural requirements for HDAC6-specific inhibitors and to derive predictive model which can be used for designing new selective HDAC6 inhibitors, a three-dimensional quantitative structure-activity relationship study was carried out on a diverse set of ligands using common feature-based pharmacophore alignment followed by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models displayed high correlation of 0.978 and 0.991 for best CoMFA and CoMSIA models, respectively, and a good statistical significance. The model could be used for predicting activities of the test set compounds as well as for deriving useful information regarding steric, electrostatic, hydrophobic properties of the molecules used in this study. Further, the training and test set molecules were docked into the HDAC6 binding site and molecular dynamics was carried out to suggest structural requirements for design of new inhibitors.

Quantitative structure–activity relationship analysis of thiophene derivatives to explore the structural requirements for c-Jun NH2-terminal kinase 1 inhibitory activity

Background: With an aim to design a validated two-dimensional quantitative structure–activity relationship (2D QSAR) model, a probe was executed on a series of reported c-Jun NH2-terminal kinase-1 (JNK1) inhibitors, exhibiting selectivity toward JNKs (and not other members of MAPK family). Objective: The present work focused on obtaining valuable insights from the structural architecture of the selected compounds and their effects on JNK1 inhibitory activity. The present work deciphers the importance of descriptive variables, namely Verloop L (Subst. 1), Bond Dipole Moment (Subst. 2), LogP (Subst. 1), Balaban Topological index (Subst. 1), and VAMP Total Dipole (whole molecule), in molecules possessing JNK1 inhibitory profile. Results: These explanatory variables, obtained after iteratively reducing the data, did not only provide us with the substantial evidence pertaining to the dependence of bioactivity on the structural features of molecules, but also suggested the measures to optimize the selected compounds so as to obtain potent JNK1 inhibitors with good selectivity profile. Based on these distinct descriptors, exhibiting no apparent intercorrelation and manifesting good correlation with biological activity, a 2D QSAR model was generated. Conclusion: Robustness of the developed model was evaluated by performing multiple linear regression, partial least square, and artificial neural network studies. The reliability and predictive ability of the developed model was ascertained through the values of standard statistical parameters, such as s = 0.38, F = 97.22, r = 0.95, r2 = 0.90, and r2cv = 0.88, for the training set compounds. The generated model was validated through the test set compounds, as well as by leave one out method.

Quantitative structure-property relationship studies for the prediction of the vapor pressure of volatile organic compounds

A theoretical model (QSPR) using multiple linear regression analysis for predicting the vapor pressure (pv) of volatile organic compounds (VOCs) has been developed. A series of 51 compounds were analyzed by multiple linear regression analysis. First, the data set was separated arbitrarily into a training set (39 chemicals) and a test set (12 chemicals) for statistical external validation. A four-dimensional model was developed using as independent variables theoretical descriptors derived from Dragon software when applying the GA (genetic algorithm)?VSS (variable subset selection) procedure. The obtained model was used to predict the vapor pressure of the test set compounds, and an agreement between experimental and predicted values was verified. This model, with high statistical significance (R2 = 0.9090, Q2 LOO = 0.8748, Q2 ext = 0.8307, s = 0.24), could be used adequately for the prediction and description of the log pv value of other VOCs. The applicability domain of MLR model was investigated using a William?s plot to detect outliers and outsides compounds.

Design of antimalarial transmission blocking agents: Pharmacophore mapping of ligands active against stage-V mature gametocytes of Plasmodium falciparum

The discovery of transmission-blocking (T-B) agents is crucial for preventing and complete removal of malaria infection. However, most of the existing antimalarials are only active against the asexual stages of Plasmodium parasite, but ineffective against the sexual stage (gametocytes). In this background, we have developed pharmacophore models against the stage-V mature gametocytes of P. falciparum parasites. The pharmacophore model (Hypo-1) showed five pharmacophoric features namely, one hydrogen bond donor (HBD), one hydrophobic aliphatic (HYAl), one ring aromatic (RA), and two hydrophobic aromatic (HYAr) essential for the anti-gametocytic activity. The amino, methyl, fused phenyl ring of the quinazoline heterocycle, two phenyl rings of biphenyl moiety (HBD, HYAl, HYAr1, HYAr2 and RA) are the crucial features responsible for the non-specific anti-gametocytic activity (PfG). Subsequently, the model (Hypo-2) developed against the stage-V female gametocytes (PffG) showed the contribution of three pharmacophoric features namely, two hydrogen bond acceptor (HYA) and one RA required for the anti-gametocytic activity. The sulfhydryl, imine and pyridyl groups are observed to be essential for anti-gametocytic activity against female gametocytes. Both the models (PfG and PfGG) showed the classification accuracies of 78.26 and 71.64% for training set compounds and 60.80 and 60.18% for the test set compounds, respectively, for classification of compounds into higher and lower active classes. Also, both the models were found to retain the higher active compounds (IC50 <100 nM) in top 1% of total compounds (actives and decoys) as observed after screening the decoy set compounds.Communicated by Ramaswamy H Sarma

QSPR modelling for prediction of glass transition temperature of diverse polymers

ABSTRACTThe glass transition temperature is a vital property of polymers with a direct impact on their stability. In the present study, we built quantitative structure–property relationship models for the prediction of the glass transition temperatures of polymers using a data set of 206 diverse polymers. Various 2D molecular descriptors were computed from the single repeating units of polymers. We derived five models from different combinations of six descriptors in each case by employing the double cross-validation technique followed by partial least squares regression. The selected models were subsequently validated by methods such as cross-validation, external validation using test set compounds, the Y-randomization (Y-scrambling) test and an applicability domain study of the developed models. All of the models have statistically significant metric values such as r2 ranging from 0.713–0.759, Q2 ranging from 0.662-0.724 and ranging 0.702–0.805. Finally, a comparison was made with recently published models, though the previous models were based on a much smaller data set with limited diversity. We also used a true external set to demonstrate the performance of our developed models, which may be used for the prediction and design of novel polymers prior to their synthesis.

Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors.

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure–activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R2 (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq2 (leave-one-out correlation coefficient) = 0.864, F (Fisher’s test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and Next (number of compounds in external test set) = 20, R2 = 0.853, (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R2 = 0.925, LOOq2 = 0.924, F = 950.686, RMS = 0.447 for the training set, and Next = 20, R2 = 0.896, = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study

Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.

QSAR studies on some C14-urea tetrandrine compounds as potent anti-cancer against Leukemia cell line (K562)

This research applied Quantitative Structure Activity Relationship (QSAR) technique in developing a Multiple-Linear Regression (MLR) model using Genetic Functional Algorithm (GFA) method in selecting relevant molecular descriptors from the structures of 24 C14-urea tetrandrine compounds. Firstly, the compounds were optimized at Density Functional Theory (DFT) level using Becke’s three-parameter Lee-Yang-Parr hybrid functional (B3LYP) with the 6-31G* basis set in the Spartan 14 Version 1.1.4 software. The molecular descriptors were calculated using Padel- software, and the results were divided in to training and test set. A model was built from the training set with internal validation parameter R2train as 0.910403. The external validation of the model was carried out using the test set compounds with validation parameter R2test as 0.6443 which passed the criteria for acceptability of a QSAR model globally. The coefficient of determination (𝑐𝑅2𝑝) parameter was calculated as 0.819296 which is greater than 0.5, this affirms that the generated model is robust. Furthermore, AST4p, GATS8v and MLFER are the descriptors in the model with positive mean effect of 0.089972855, 0.909814859 and 0.000212286 respectively. This study inferred that there will be positive influence on the inhibitory concentrations when the each descriptor value increases

4D-QSAR Studies Using a New Descriptor of the Klopman Index: Antibacterial Activities of Sulfone Derivatives Containing 1, 3, 4-Oxadiazole Moiety Based on MCET Model.

In this paper, we have introduced a new atomic descriptor with Klopman index to determine the local reactive sites of the molecular systems during electrophilic, and nucleophilic attacks. This index, similar to other local reactivity descriptors but more advanced, has been used as a realistic descriptor to discover new aspects of molecular structure. Nonlinear Least Squares (NLLS) methods to define the parameters maximizing the fit between the observed points and the computed simulation results were performed according to the Levenberg- Marquardt (LM) algorithm. We have attempted to demonstrate the structural properties of compounds that contribute not only basic pharmacophore (b-Pha) but also positive (Auxiliary Group- AG) or negative (Anti-Pharmacophore Shielding-APS) due to the new local atomic reactivity. In the 4D-QSAR study, nonparametric regression analysis was used to determine the adjustable constants. Using the leave One Out Cross-Validation (LOO-CV), antibacterial activities (pEC50-μM) were predicted as r2 loo-cv (q2) = 0.979, r2 pred (r2) = 0.911, respectively, for 27 training sets and 9 test set compounds. Also, the rm2 (overall) value, which indicates the closeness between the predicted and corresponding observed data, was calculated to be 0.957. The model obtained by the Molecular Conformer Electron Topological (MCET) method was compared with the q2 loo-cv and R2 non-cv values determined by the CoMFA and CoMSIA methods and more satisfactory results were obtained.

Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

By using the molecular docking and 4D-QSAR analysis, it is aimed to find the interaction points in the receptor binding site of transforming growth factor-beta (TGF-beta) used to inhibit invasion and metastasis. To elucidate the interaction points of receptor, different types of local reactive descriptor (LRD) of ligands have been used. Activity values related to interaction energy between the ligand-receptor (L–R) were determined by nonlinear least squares (NLLS) using the Levenberg-Marquardt (LM) algorithm. Using the Molecule Comparative Electron Topology (MCET) method, the 3D pharmacophore model (3D-PhaM) was obtained after alignment and superimposition of the molecules, and also confirmed by molecular docking method. With the leave one out-cross validation (LOO-CV) method, the best predictions are q2 or rCV2 = 0.789 for the 51 compounds in the internal training set and r2 = 0.785 for the 13 compounds in the external test set. Furthermore, the predictive capability of the advanced QSAR model is more precisely calculated with the rm2 metric (rm2 = 0.769).