Docking alignment-3D-QSAR of a new class of potent and non-chiral indole-3-carboxamide-based renin inhibitors

2011 ◽  
Vol 76 (12) ◽  
pp. 1447-1469 ◽  
Author(s):  
Jahan B. Ghasemi ◽  
Somayeh Pirhadi
Keyword(s):  
Cross Validation ◽  
3D Qsar ◽  
Initial Orientation ◽  
Test Set ◽  
Docking Analysis ◽  
Renin Inhibitors ◽  
Contour Maps ◽  
New Class ◽  
Qsar Models ◽  
Leave One Out

Using generated conformations from docking analysis by CDOCKER algorithm, some 3D-QSAR models; CoMFA region focusing (CoMFA-RF) and CoMSIA have been created on a series of a new class of potent and non-chiral renin inhibitors. The satisfactory predictions were obtained by CoMFA-RF and CoMSIA based on docking alignment in comparison to CoMFA. Robustness and predictability of the models were further verified by using the test set, cross validation (leave one out and leave ten out), bootstrapping, and progressive scrambling. All-orientation search (AOS) strategy was used to acquire the best orientation and minimize the effect of the initial orientation of aligned compounds. The results of 3D-QSAR models are in agreement with docking results. Moreover, the resulting 3D CoMFA-RF/ CoMSIA contour maps and corresponding models were applied to design new and more active 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.

Molecular Docking and 3D-QSAR Studies of 4-Phenylpiperidine Derivatives as μ-Opioid Agonists

2011 ◽  
Vol 361-363 ◽  
pp. 263-267 ◽  
Author(s):  
Ming Liu ◽  
Wen Xiang Hu ◽  
Xiao Li Liu
Keyword(s):  
Electrostatic Interactions ◽  
Biological Activities ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Studies ◽  
Chemical Structures ◽  
Opioid Agonists ◽  
Qsar Models ◽  
Leave One Out

A predictive 3D-QSAR model which correlates the biological activities with the chemical structures of a series of 4-phenylpiperidine derivatives as μ opioid agonists was developed by means of comparative molecular field analysis (CoMFA). The stabilities of the 3D-QSAR models were verified by the leave-one-out cross-validation method. Moreover, the predictive capabilities of the models were validated by an external test set. Best predictions were obtained with CoMFA standard model(q2=0.504, N=6, r2=0.968) which revealed how steric and electrostatic interactions contribute to agonists bioactivities, and provided us with important information to understand the interaction of agonists and μ opioid receptor .

QSAR study of amidino bis-benzimidazole derivatives as potent anti-malarial agents against Plasmodium falciparum

2013 ◽  
Vol 67 (11) ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Chanin Nantasenamat ◽  
Chartchalerm Isarankura-Na-Ayudhya ◽  
Virapong Prachayasittikul
Keyword(s):  
Plasmodium Falciparum ◽  
Correlation Coefficient ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Sum Of Squares ◽  
Atomic Masses ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Qsar Models ◽  
Leave One Out

AbstractA data set of amidino bis-benzimidazoles, in particular 2′-arylsubstituted-1H,1′H-[2,5′]bisbenzimidazolyl-5-carboximidine derivatives with anti-malarial activity against Plasmodium falciparum was employed in investigating the quantitative structure-activity relationship (QSAR). Quantum chemical and molecular descriptors were obtained from B3LYP/6-31g(d) calculations and Dragon software, respectively. Significant variables, which included total energy (E T), highest occupied molecular orbital (HOMO), Moran autocorrelation-lag3/weighted by atomic masses (MATS3m), Geary autocorrelation-lag8/weighted by atomic masses (GATS8m), and 3D-MoRSEsignal 11/weighted by atomic Sanderson electronegativities (Mor11e), were used in the construction of QSAR models using multiple linear regression (MLR) and artificial neural network (ANN). The results indicated that the predictive models for both the MLR and ANN approaches using leave-one-out cross-validation afforded a good performance in modelling the anti-malarial activity against P. falciparum as observed by correlation coefficients of leave-one-out cross-validation (R LOO-CV) of 0.9760 and 0.9821, respectively, root mean squared error of leave-one-out cross-validation (RMSELOO-CV) of 0.1301 and 0.1102, respectively, and predictivity of leave-one-out cross-validation (Q LOO-CV2) of 0.9526 and 0.9645, respectively. Model validation was performed using an external testing set and the results suggested that the model provided good predictivity for both MLR and ANN models with correlation coefficient of the external set (R Ext) values of 0.9978 and 0.9844, respectively, root mean squared error of the external set (RMSEExt) of 0.0764 and 0.1302 respectively, and predictivity of the external set (Q Ext2) of 0.9956 and 0.9690, respectively. Furthermore, the robustness of the QSAR models is corroborated by a number of statistical parameters, comprising adjusted correlation coefficient (R Adj2), standard deviation (s), predicted residual sum of squares (PRESS), standard error of prediction (SDEP), total sum of squares deviation (SSY), and quality factor (Q). The QSAR models so constructed provide pertinent insights for the future design of anti-malarial agents.

scholarly journals Structure Activity Relationships, QSAR Modeling and Drug-Like Calculations of TP Inhibition of 1,3,4-Oxadiazoline-2-Thione Derivatives

2014 ◽  
Vol 37 ◽  
pp. 113-124 ◽  
Author(s):  
Zineb Almi ◽  
Salah Belaidi ◽  
Touhami Lanez ◽  
Noureddine Tchouar
Keyword(s):  
Predictive Power ◽  
Cross Validation ◽  
Molecular Descriptor ◽  
Qsar Model ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Structure Activity ◽  
Qsar Models ◽  
Leave One Out

QSAR studies have been performed on twenty-one molecules of 1,3,4-oxadiazoline-2-thiones. The compounds used are among the most thymidine phosphorylase (TP) inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and TP inhibition of the 1,3,4-oxadiazoline-2-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, Pol, MR, MV, and MW, qO1, SAG, for the TP inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.

COMPARISON OF THREE 3D-QSAR METHODS USING A NOVEL CLASS OF MURF INHIBITORS

2007 ◽  
Vol 06 (01) ◽  
pp. 63-80 ◽  
Author(s):  
DE-XIN KONG ◽  
WEI-LIANG ZHU ◽  
DA-LEI WU ◽  
XU SHEN ◽  
HUA-LIANG JIANG
Keyword(s):  
Cross Validation ◽  
Molecular Similarity ◽  
Predictive Ability ◽  
3D Qsar ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Virtual Database ◽  
Similarity Indices ◽  
Qsar Models ◽  
Good Agreement

MurF was considered as an attractive target for new antibacterial discovery. In this paper, three QSAR methods were employed, viz. comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram QSAR (HQSAR), to derive highly predictive QSAR models for designing novel MurF inhibitors and comparing different 3D-QSAR/alignment methods. QSAR models with high predictive ability for MurF inhibitors were successfully constructed in terms of cross-validation q2, standard error and predictive coefficient r2, which were around 0.70, 0.55 and 0.99, respectively. All the models from different methods were in good agreement with each other. Compounds with indeterminate activities were used as a test set; results showed that CoMSIA had the best predictive ability, followed by HQSAR and CoMFA. Based on these models, some key features for designing new MurF inhibitors were identified. A virtual database screen process was proposed based on the combination of these models.

In Silico Design of New B-Raf Kinase Type-II Inhibitors Through Combined Molecular Modeling Studies

2019 ◽  
Vol 16 (5) ◽  
pp. 570-583
Author(s):  
Weineng Zhou ◽  
Shuai Lu ◽  
Yanmin Zhang ◽  
Lingfeng Yin ◽  
Lu Zhu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Molecular Modeling ◽  
Binding Site ◽  
Biological Activities ◽  
3D Qsar ◽  
Type Ii ◽  
Contour Maps ◽  
Raf Kinase ◽  
Linear Relationships ◽  
Qsar Models

Background:B-Raf has become an important and exciting therapeutic cancer target.Methods:In the present work, molecular modeling protocols like molecular docking, MM/GBSA calculations, 3D-QSAR and binding site detection were performed on a dataset of 41 Type II inhibitors. Molecular docking was applied to explore the detailed binding process between the inhibitors and B-Raf kinase. Furthermore, the good linear relationships between G-Scores and MM/GBSA calculated and the experimental activity were shown. The satisfactory CoMFA and CoMSIA were constructed based on the conformations obtained by molecular docking.Results:The key structural requirements for increasing biological activity were verified by analyzing 3D contour maps of the 3D-QSAR models. FTMap and SiteMap were also used to detect the more efficient active binding site.Conclusion:New inhibitors were synthesized and the biological activities were evaluated, the results further validated our design strategy.

MISS ONE OUT: A CROSS-VALIDATION METHOD UTILIZING INDUCED TEACHER NOISE

2013 ◽  
Vol 27 (07) ◽  
pp. 1351003
Author(s):  
AHMET ALPTEKIN ◽  
OLCAY KURSUN
Keyword(s):  
Cross Validation ◽  
Training Sample ◽  
Experimental Results ◽  
The Other ◽  
Training Set ◽  
Test Set ◽  
Validation Method ◽  
Trained Classifier ◽  
Benchmark Datasets ◽  
Leave One Out

Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.

scholarly journals Optimal Division of Molecules Into Training And Test Sets With A New Tool To Predict Pharmacophore In 3D-QSAR

2021 ◽  
Author(s):  
Tuğba Alp Tokat ◽  
Burçin Türkmenoğlu ◽  
Yahya Güzel
Keyword(s):  
Cross Validation ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Interaction Point ◽  
One Dimensional ◽  
System A ◽  
Test Sets ◽  
Leave One Out ◽  
External Test ◽  
Statistical Results

Abstract According to the descriptors in the pharmacophore model, dividing molecules into training and test sets serves to create a good model. It is difficult to track the Local Reactive Descriptor (LRD) effect of the pharmacophore at each interaction point in the 3D metric system. A subset of clusters of atoms can correspond to all or part of the pharmacophore structure. In this study, the multidimensional system of the subset was reduced to a one-dimensional index and the Vector Fingerprint Functions (VFF) of the molecules were created. Models were established by dividing molecules with close and similar VFFs into training and test sets. Sub-clusters were examined for all molecules by applying the Genetic Algorithm (GA). The model was predicted using the Leave One Out-Cross Validation (LOO-CV) method and verified with an external test set. The statistical results of the model obtained according to the division in the new method we developed (Q2 = 0.604 and R2 = 0.760 for training-80 and external test-20 sets, respectively) were compared with random and manual division results.

scholarly journals 3D QSAR of Pyrrolo Pyrimidine and Thieno Pyrimidines as Human Thymidylate Synthase Inhibitors

2012 ◽  
Vol 9 (4) ◽  
pp. 1699-1710 ◽  
Author(s):  
K. Meena Kumari ◽  
L. Yamini ◽  
M. Vijjulatha
Keyword(s):  
Thymidylate Synthase ◽  
Rational Design ◽  
Cross Validation ◽  
3D Qsar ◽  
Structure Activity ◽  
Dna Biosynthesis ◽  
Contour Plots ◽  
Leave One Out ◽  
Thieno Pyrimidine ◽  
Relationship Of

Thymidylate synthase (TS) is a crucial enzyme for DNA biosynthesis and many nonclassical lipophilic antifolates targeting this enzyme are quite efficient and encouraging as antitumor drugs. We report 3D-QSAR analyses on pyrrolo pyrimidine and thieno pyrimidine antifolates to contemplate the mechanism of action and structure-activity relationship of these molecules. By applying leave-one-out (LOO) cross-validation study, cross-validated q2value of 0.523 and 0.566 for CoMFA Ligand based (LB) and Receptor based (RB), 0.516 and 0.471 for CoMSIA LB and RB respectively. while the non-cross-validated r2values were found to be 0.974 and 0.969 for CoMFA LB and RB, 0.983 and 0.972 for CoMSIA LB and RB respectively. The models were graphically interpreted using CoMFA and CoMSIA contour plots. The results obtained from this study were used for rational design of potent inhibitors against thymidylate synthase.

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