scholarly journals A Predictive HQSAR Model for a Series of Tricycle Core Containing MMP-12 Inhibitors with Dibenzofuran Ring

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jamal Shamsara ◽  
Ahmad Shahir-Sadr
Keyword(s):  
Predictive Ability ◽  
Binding Pocket ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Training Set ◽  
Color Code ◽  
Test Set ◽  
Relationship Model ◽  
Predicted Values ◽  
Good Agreement

MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q2 value of 0.697 and conventional r2 value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (rpred2=0.8733). Then, the external validity of the model was confirmed by Golbraikh-Tropsha and rm2 metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors.

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

2018 ◽  
Vol 19 (11) ◽  
pp. 3423 ◽  
Author(s):  
Ting Wang ◽  
Lili Tang ◽  
Feng Luan ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Correlation Coefficient ◽  
Binary Mixtures ◽  
Quantitative Structure Activity Relationship ◽  
Training Set ◽  
Statistical Parameters ◽  
Test Set ◽  
Qsar Models ◽  
Forward Stepwise ◽  
Leave One Out ◽  
External Test

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, qext2 (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, qext2 = 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.

scholarly journals Structural Features of Quercetin Derivatives by Using Pharmaco-phore Modeling Approach

2016 ◽  
Vol 3 (1) ◽  
pp. 79-98
Author(s):  
Nixon Mendez ◽  
Md. Afroz Alam
Keyword(s):  
Lung Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Pharmacophore Model ◽  
Cancer Cell Lines ◽  
Structural Features ◽  
Cancer Drug ◽  
Training Set ◽  
Test Set ◽  
Quercetin Derivatives

Background:Quercetin which is a natural occurring flavonoid, exert a direct pro-apoptotic effect on tumor cells by blocking the growth of several cancer cell lines at different phases of the cell cycle. Quercetin derivatives have attracted considerable attention for their cytotoxity against human cancer cell lines. In this study the derivatives of Quercetin were used for docking followed by pharmacophore modeling for studying the 3D features and configurations responsible for biological activity of structurally diverse compounds.Objective:To develop a model which depicts the crucial structural features responsible for anti-lung cancer activities.Method:A robust pharmacophore developed for the receptor have been analyzed to identify potential areas of selectivity in the hyperspace of 3D pharmacophores that may lead to the discovery of anti-lung cancer drug or such compounds which could serve as templates for the design of new molecules as potential anti lung cancer agents.Results:The generated best pharmacophore hypothesis yielded a statistically significant 3D-QSAR model, with a correlation coefficient of R2= 0.86 for training set and R2= 0.76 for the test set molecules. The Cross validation regression coefficient is Q2= 0.84 for training set and Q2= 0.5 for test set molecules.Conclusion:The R2and Q2reveals that pharmacophore model provide insights into the structural and chemical features of the EGFR inhibitors of Quercetin derivatives that can be used as lead compound for further synthesis as well as for screening other similar novel inhibitors of EGFR.

scholarly journals Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3271 ◽  
Author(s):  
Imane Naboulsi ◽  
Aziz Aboulmouhajir ◽  
Lamfeddal Kouisni ◽  
Faouzi Bekkaoui ◽  
Abdelaziz Yasri
Keyword(s):  
Tyrosine Kinases ◽  
Kinase Inhibitors ◽  
Structural Features ◽  
Ann Model ◽  
Training Set ◽  
Test Set ◽  
Lyn Kinase ◽  
Physico Chemical ◽  
The Mean ◽  
Artificial Neural Network Ann

Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors.

In-Silico QSAR Modelling of Predicted Rho Kinase Inhibitors Against Cardio Vascular Diseases

2019 ◽  
Vol 15 (5) ◽  
pp. 421-432
Author(s):  
Seema Kesar ◽  
Sarvesh Paliwal ◽  
Swapnil Sharma ◽  
Pooja Mishra ◽  
Monika Chauhan ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
Inflammatory Diseases ◽  
Smooth Muscles ◽  
Rho Kinase ◽  
Vascular Diseases ◽  
Docking Study ◽  
Binding Pocket ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Qsar Analysis

Background: : Rho-kinase is an essential downstream target of GTP-binding protein RhoA, and plays a crucial role in the calcium-sensitization pathway. Rho-kinase pathway is critically involved in phosphorylation state of myosin light chain, leading to increased contraction of smooth muscles. Inhibition of this pathway has turned out to be a promising target for several indications such as cardiovascular diseases, glaucoma and inflammatory diseases. Methods:: The present work focuses on a division-based 2D quantitative structure-activity relationship (QSAR) analysis along with a docking study to predict structural features that may be essential for the enhancement of selectivity and potency of the target compounds. Furthermore, a set of indoles and azaindoles were also projected based on the regression equation as novel developments. Molecular docking was applied for exploring the binding sites of the newly predicted set of compounds with the receptor. Results: : Results of the docked conformations suggested that introduction of non-bulky and substituted groups in the hinge region of ROCK-II ATP binding pocket would improve the activity by decreasing the bulkiness or length of the compounds. Conclusion: : ADME studies were performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.

A new, fully validated and interpreted quantitative structure-activity relationship model of p-aminosalicylic acid derivatives as neuraminidase inhibitors

2013 ◽  
Vol 67 (5) ◽  
Author(s):  
Ana Hartmman ◽  
Daniela Jornada ◽  
Eduardo Melo
Keyword(s):  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Neuraminidase Inhibitors ◽  
Aminosalicylic Acid ◽  
Randomization Tests ◽  
Qsar Study ◽  
Relationship Model ◽  
Mechanism Of Interaction

AbstractA multivariate QSAR study with a set of 34 p-aminosalicylic acid derivatives, described as neuraminidase inhibitors of the H1N1 viruses, is presented in this work. The variable selection was performed with the Ordered Predictors Selection (OPS) algorithm and the model was built with the Partial Least Squares (PLS) regression method. Leave-N-out cross-validation and y-randomization tests showed that the model was robust and free from chance correlation. The external predictive ability was superior to the 3D-QSAR model previously published. Moreover, it was possible to perform a mechanistic interpretation, where the descriptors referred directly to the mechanism of interaction with the neuraminidase.

scholarly journals Profiling the structural determinants of aminoketone derivatives as hNET and hDAT reuptake inhibitors by field-based QSAR based on molecular docking

2021 ◽  
Vol 29 ◽  
pp. 257-273
Author(s):  
Panpan Wang ◽  
Chenxi Jing ◽  
Pei Yu ◽  
Meng Lu ◽  
Xiaobo Xu ◽  
...  
Keyword(s):  
Neurodegenerative Disorders ◽  
Medicinal Chemistry ◽  
Structural Features ◽  
Clinical Settings ◽  
Training Set ◽  
Structural Determinants ◽  
Test Set ◽  
Contour Maps ◽  
Pharmacokinetic Properties ◽  
Derivatives Of

BACKGROUND: Bupropion, one of the dual norepinephrine and dopamine reuptake inhibitors (NDRIs), is an aminoketone derivative performed effect in improving cognitive function for depression. However, its therapeutic effect is unsatisfactory due to poor clinical response, and there are only few derivatives in pre-clinical settings. OBJECTIVE: This work attempted to elucidate the essential structural features for the activity and designed a series of novel derivatives with good inhibitive ability, pharmacokinetic and medicinal chemistry properties. METHODS: The field-based QSAR of aminoketone derivatives of two targets were established based on docking poses, and the essential structural properties for designing novel compounds were supplied by comparing contour maps. RESULTS: The selected two models performed good predictability and reliability with R2 of 0.8479 and 0.8040 for training set, Q2 of 0.7352 and 0.6266 for test set respectively, and the designed 29 novel derivatives performed no less than the highest active compound with good ADME/T pharmacokinetic properties and medicinal chemistry friendliness. CONCLUSIONS: Bulky groups in R1, bulky groups with weak hydrophobicity in R3, and potent hydrophobic substituted group with electronegative in R2 from contour maps provided important insights for assessing and designing 29 novel NDRIs, which were considered as candidates for cognitive dysfunction with depression or other related neurodegenerative disorders.

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors

2020 ◽  
Vol 16 (5) ◽  
pp. 654-666 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Yao Xi ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Training Set ◽  
Test Set ◽  
Structure Activity ◽  
Consensus Models ◽  
Hiv 1

Background: HIV-1 Integrase (IN) is an important target for the development of the new anti-AIDS drugs. HIV-1 LEDGF/p75 inhibitors, which block the integrase and LEDGF/p75 interaction, have been validated for reduction in HIV-1 viral replicative capacity. Methods: In this work, computational Quantitative Structure-Activity Relationship (QSAR) models were developed for predicting the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors. We collected 190 inhibitors and their bioactivities in this study and divided the inhibitors into nine scaffolds by the method of T-distributed Stochastic Neighbor Embedding (TSNE). These 190 inhibitors were split into a training set and a test set according to the result of a Kohonen’s self-organizing map (SOM) or randomly. Multiple Linear Regression (MLR) models, support vector machine (SVM) models and two consensus models were built based on the training sets by 20 selected CORINA Symphony descriptors. Results: All the models showed a good prediction of pIC50. The correlation coefficients of all the models were more than 0.7 on the test set. For the training set of consensus Model C1, which performed better than other models, the correlation coefficient(r) achieved 0.909 on the training set, and 0.804 on the test set. Conclusion: The selected molecular descriptors show that hydrogen bond acceptor, atom charges and electronegativities (especially π atom) were important in predicting the activity of HIV-1 integrase LEDGF/p75-IN inhibitors.

Extracting structural requirements for activity of GPR119 agonists: a hologram quantitative structure activity relationship (HQSAR) study

2014 ◽  
Vol 92 (7) ◽  
pp. 670-676 ◽  
Author(s):  
Apoorva G. Ugarkar ◽  
Premlata K. Ambre ◽  
Evans C. Coutinho ◽  
Santosh Nandan ◽  
Raghuvir R.S. Pissurlenkar
Keyword(s):  
Biological Activity ◽  
Fragment Size ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Training Set ◽  
Qsar Study ◽  
Drug Candidates ◽  
Glucagon Like Peptide 1 ◽  
Experimental Values ◽  
External Test

GPR119 is a potential target for the treatment of diabetes mellitus. GPR119 agonists minimize the side-effects observed with sulphonyl ureas and glucagon-like peptide 1 analogs. Various reported GPR119 agonists from various patents were selected for the study and a 2D-QSAR study (HQSAR) was carried out. Fifty-five molecules were selected for the study. The study was performed on a training set of 40 structurally diverse molecules with reported biological activity. The most significant HQSAR model (q2 = 0.87, r2 = 0.99) was obtained using atoms, bond, connection, and acceptor and donor as fragment distinction. The fragment size was kept at 4–7. The predictive ability of the model was evaluated by an external test set containing 15 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The important fragments determined by the study were used to design new drug candidates having increased biological activity and comparable physicochemical properties.

scholarly journals Using Decision Tree to Predict Response Rates of Consumer Satisfaction, Attitude, and Loyalty Surveys

2019 ◽  
Vol 11 (8) ◽  
pp. 2306 ◽  
Author(s):  
Jian Han ◽  
Miaodan Fang ◽  
Shenglu Ye ◽  
Chuansheng Chen ◽  
Qun Wan ◽  
...  
Keyword(s):  
Decision Tree ◽  
Consumer Satisfaction ◽  
Response Rate ◽  
Regression Tree ◽  
Response Rates ◽  
Classification And Regression Tree ◽  
Training Set ◽  
Test Set ◽  
Predictors Of Response ◽  
Predicted Values

Response rate has long been a major concern in survey research commonly used in many fields such as marketing, psychology, sociology, and public policy. Based on 244 published survey studies on consumer satisfaction, loyalty, and trust, this study aimed to identify factors that were predictors of response rates. Results showed that response rates were associated with the mode of data collection (face-to-face > mail/telephone > online), type of survey sponsors (government agencies > universities/research institutions > commercial entities), confidentiality (confidential > non-confidential), direct invitation (yes > no), and cultural orientation (individualism > collectivism). A decision tree regression analysis (using classification and regression Tree (C&RT) algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained a linear correlation coefficient (0.578) between the predicted values and actual values, which was higher than the corresponding coefficient of the traditional linear regression model (0.423). A decision tree analysis (using C5.0 algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained an overall accuracy of 78.26% in predicting whether a survey had a high (>50%) or low (<50%) response rate. Direct invitation was the most important factor in all three models and had a consistent trend in predicting response rate.

Three Dimensional Quantitative Structure Activity Relationship and Molecular Docking Studies of Flavonoids as Reverse Transcriptase Inhibitors

2021 ◽  
Vol 6 (1) ◽  
pp. 33-39
Author(s):  
Bikash Kumar Sarkar ◽  
Nabanita Giri
Keyword(s):  
Correlation Coefficient ◽  
Three Dimensional ◽  
Predictive Ability ◽  
Binding Pocket ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Field Analysis ◽  
Comfa Model ◽  
Bonding Interaction ◽  
Hiv 1

A set of 29 flavonoid molecules are used to generate comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. The best CoMFA model showed a cross-validated correlation coefficient (q2) = 0.762, noncross- validated correlation coefficient (r2) = 0.939, standard error of estimate (S) = 0.038 and F = 396. And that for CoMSIA model were q2 = 0.758, r2 = 0.957, S = 0.063 and F = 236. The models show a high predictive ability, validated by 11 favonoid molecules. The docking studies shows the hydrogen bonding interaction is mostly responsible for binding of the flavonoids molecules in the binding pocket of HIV 1-RT protein (3HVT.pdb).

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