A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

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

International Journal of Molecular Sciences ◽

10.3390/ijms19113423 ◽

2018 ◽

Vol 19 (11) ◽

pp. 3423 ◽

Cited By ~ 12

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.

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QSAR Study of (5-Nitroheteroaryl-1,3,4-Thiadiazole-2-yl) Piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

Advances in Physical Chemistry ◽

10.1155/2018/2569129 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Abdellah Ousaa ◽

Bouhya Elidrissi ◽

Mounir Ghamali ◽

Samir Chtita ◽

Adnane Aouidate ◽

...

Keyword(s):

External Validation ◽

Correlation Coefficients ◽

Predictive Ability ◽

Quantitative Structure Activity Relationship ◽

Qsar Study ◽

Qsar Analysis ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Leave One Out ◽

New Compounds

To search for newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5-nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds using several statistical tools. The multiple linear regression (MLR), nonlinear regression (RNLM), and artificial neural network (ANN) models were developed using 30 molecules having pIC50 ranging from 3.155 to 5.046. The best generated MLR, RNLM, and ANN models show conventional correlation coefficients R of 0.750, 0.782, and 0.967 as well as their leave-one-out cross-validation correlation coefficients RCV of 0.722, 0.744, and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients Rtest of 0.840, 0.850, and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William’s plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds.

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QSAR and docking studies of 3, 5-dimethylpyrazole as potent inhibitors of Phosphodiesterase-4

Journal of Drug Delivery and Therapeutics ◽

10.22270/jddt.v11i1-s.4718 ◽

2021 ◽

Vol 11 (1-s) ◽

pp. 86-93

Author(s):

Hiba Hashim Mahgoub Mohamed ◽

Amna Bint Wahab Elrashid Mohammed Hussien ◽

Ahmed Elsadig Mohammed Saeed

Keyword(s):

Molecular Docking ◽

Correlation Coefficient ◽

External Validation ◽

Quantitative Structure Activity Relationship ◽

Docking Studies ◽

Pyrazole Derivatives ◽

Qsar Study ◽

Furan Moiety ◽

The Stability ◽

Leave One Out

A quantitative structure-activity relationship (QSAR) study was performed to develop a model on a series of 3, 5-dimethylpyrazole containing furan moiety derivatives which exhibited considerable inhibitory activity against PDE4B. The obtained model has correlation coefficient (r) of 0.934, squared correlation coefficient (r2) of 0.872, and leave-one-out (LOO) cross-validation coefficient (Q2) value of 0.733. The predictive power of the developed model was confirmed by the external validation which has (r2) value of 0.812. These parameters confirm the stability and robustness of the model to predict the activity of a new designed set of 3,5-dimethyl-pyrazole derivatives (I-XV), results indicated that the compound III, V, XIII, and XV showed the strongest inhibition activity (IC50 = 0.2813, 0.5814, 0.6929, 0.6125μM, respectively) against PDE4B compared to the reference rolipram with (IC50=1.9μM). Molecular docking was performed on a new designed compound with PDE4B protein (3o0j). Docking results showed that compounds (X and IX) have high docking affinity of -36.2037 and -33.2888 kcal/mol respectively. Keywords: QSAR, molecular docking, pyrazole derivatives, PDE4 inhibitors, anti-inflammatory.

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QSAR studies for the acute toxicity of nitrobenzenes to the Tetrahymena pyriformis

Journal of the Serbian Chemical Society ◽

10.2298/jsc130910025w ◽

2014 ◽

Vol 79 (9) ◽

pp. 1111-1125 ◽

Cited By ~ 3

Author(s):

Dan-Dan Wang ◽

Lin-Lin Feng ◽

Guang-Yu He ◽

Hai-Qun Chen

Keyword(s):

Statistical Significance ◽

External Validation ◽

Tetrahymena Pyriformis ◽

Quantitative Structure Activity Relationship ◽

Partial Least Square ◽

Molecular Structures ◽

Least Square ◽

Qsar Studies ◽

Qsar Models ◽

Leave One Out

Quantitative structure-activity relationship (QSAR) models play a key role in finding the relationship between molecular structures and the toxicity of nitrobenzenes to Tetrahymena pyriformis. In this work, genetic algorithm, along with partial least square (GA-PLS) was employed to select optimal subset of descriptors that have significant contribution to the toxicity of nitrobenzenes to Tetrahymena pyriformis. A set of five descriptors, namely G2, HOMT, G(Cl?Cl), Mor03v and MAXDP, was used for the prediction of the toxicity of 45 nitrobenzene derivatives and then were used to build the model by multiple linear regression (MLR) method. It turned out that the built model, whose stability was confirmed using the leave-one-out validation and external validation test, showed high statistical significance (R2=0.963, Q2LOO=0.944). Moreover, Y-scrambling test indicated there was no chance correlation in this model.

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2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

Journal of Computational Medicine ◽

10.1155/2014/765457 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Prasanna A. Datar

Keyword(s):

Staphylococcus Aureus ◽

Pseudomonas Aeruginosa ◽

Qsar Model ◽

Antibacterial Activities ◽

Quantitative Structure Activity Relationship ◽

Comparative Approach ◽

Qsar Study ◽

Qsar Analysis ◽

Leave One Out ◽

And Staphylococcus Aureus

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

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QSAR Study of N-Myristoyltransferase Inhibitors of Antimalarial Agents

Molecules ◽

10.3390/molecules23092348 ◽

2018 ◽

Vol 23 (9) ◽

pp. 2348 ◽

Cited By ~ 3

Author(s):

Letícia Santos-Garcia ◽

Marco de Mecenas Filho ◽

Kamil Musilek ◽

Kamil Kuca ◽

Teodorico Ramalho ◽

...

Keyword(s):

Quantitative Structure Activity Relationship ◽

New Drugs ◽

Protozoan Parasites ◽

Training Set ◽

First Line ◽

Qsar Study ◽

Antimalarial Agents ◽

Genus Plasmodium ◽

Qsar Models ◽

Test Sets

Malaria is a disease caused by protozoan parasites of the genus Plasmodium that affects millions of people worldwide. In recent years there have been parasite resistances to several drugs, including the first-line antimalarial treatment. With the aim of proposing new drugs candidates for the treatment of disease, Quantitative Structure–Activity Relationship (QSAR) methodology was applied to 83 N-myristoyltransferase inhibitors, synthesized by Leatherbarrow et al. The QSAR models were developed using 63 compounds, the training set, and externally validated using 20 compounds, the test set. Ten different alignments for the two test sets were tested and the models were generated by the technique that combines genetic algorithms and partial least squares. The best model shows r2 = 0.757, q2adjusted = 0.634, R2pred = 0.746, R2m = 0.716, ∆R2m = 0.133, R2p = 0.609, and R2r = 0.110. This work suggested a good correlation with the experimental results and allows the design of new potent N-myristoyltransferase inhibitors.

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Network-based piecewise linear regression for QSAR modelling

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-019-00228-6 ◽

2019 ◽

Vol 33 (9) ◽

pp. 831-844

Author(s):

Jonathan Cardoso-Silva ◽

Lazaros G. Papageorgiou ◽

Sophia Tsoka

Keyword(s):

Virtual Screening ◽

Linear Regression ◽

Predictive Accuracy ◽

Piecewise Linear ◽

Chemical Properties ◽

Structural Similarity ◽

Quantitative Structure Activity Relationship ◽

Support Vector ◽

Piecewise Linear Regression ◽

Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

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QSAR Analysis of 5-Substituted-2-Benzoyl-aminobenzoic acids as PPAR Modulator

E-Journal of Chemistry ◽

10.1155/2004/306873 ◽

2004 ◽

Vol 1 (5) ◽

pp. 243-250 ◽

Cited By ~ 3

Author(s):

R. Hemalatha ◽

L. K. Soni ◽

A. K. Gupta ◽

S. G. Kaskhedikar

Keyword(s):

Linear Regression Analysis ◽

Qsar Model ◽

Multiple Linear Regression Analysis ◽

Quantitative Structure Activity Relationship ◽

Statistical Regression ◽

Qsar Study ◽

Qsar Analysis ◽

Aminobenzoic Acids ◽

Leave One Out ◽

Α Activity

A quantitative structure activity relationship (QSAR) study on a series of analogs of 5-aryl thiazolidine-2, 4-diones with activity on PPAR-α and PPAR-γwas made using combination of various thermodynamic, electronic and spatial descriptors. Several statistical regression expressions were obtained using multiple linear regression analysis. The best QSAR model was further validated by leave one out cross validation method. The studied revealed that for dual PPAR-α/γactivity dipole-dipole energy and PMI-Z play significant role and contributed positively for PPAR-γand PPAR-α activity respectively. Thus, QSAR brings important structural insight to aid the design of dual PPAR-α /γreceptor agonist.

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QSAR modeling of antibacterial activity of some benzimidazole derivatives

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq100405050p ◽

2011 ◽

Vol 17 (1) ◽

pp. 33-38 ◽

Cited By ~ 7

Author(s):

Sanja Podunavac-Kuzmanovic ◽

Dragoljub Cvetkovic

Keyword(s):

Antibacterial Activity ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Structural Features ◽

Benzimidazole Derivatives ◽

Qsar Modeling ◽

Qsar Study ◽

Validation Procedure ◽

Leave One Out

A quantitative structure-activity relationship (QSAR) study has been carried out for training set of 12 benzimidazole derivatives to correlate and predict the antibacterial activity of studied compounds against Gram-negative bacteria Pseudomonas aeruginosa. Multiple linear regression was used to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: parameter of lipophilicity (logP) and hydration energy (HE). Good agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the generated QSAR model.

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An Effective Prediction of Biomagnification Factors for Organochlorine Pollutants

10.21203/rs.3.rs-243149/v1 ◽

2021 ◽

Author(s):

Ming Cai Zhang ◽

Hong Lin Zhai ◽

Ke Xin Bi ◽

Bin Qiang Zhao ◽

Hai Ping Shao

Keyword(s):

Correlation Coefficient ◽

Predictive Ability ◽

Structure Property ◽

Organochlorine Pollutants ◽

Structure Property Relationship ◽

Tchebichef Moments ◽

Independent Test ◽

Molecular Information ◽

Leave One Out ◽

Multivariate Image

Abstract Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570 and the correlation coefficient of prediction (Rp) for external independent test set was 0.9594. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.

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