Genetic Algorithm-multiple Linear Regression Model Research Articles

Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

Use of biomimetic chromatography and in vitro assay to develop predictive GA-MLR model for use in drug-property prediction among anti-depressant drug candidates

In recent years mental disorders, such as depression, have become an alarmingly frequent problem in modern society. Therefore, a need for novel anti-depressant agents is ever-growing. With low drug acceptance rate, industry is struggling to introduce new anti-depressant agents, and every single tool helping to change this state of things is of immense value. In this study, a set of potential novel anti-depressant derivatives with confirmed pharmacological activity was subjected to a number of assays. A group of four drug properties, assessed in early stages of drug development were chosen for those assays. Lipophilicity, membrane permeability and drug-protein binding were estimated using biomimetic chromatography methods, while metabolic stability was estimated using in-vitro enzymatic assay followed by Liquid chromatography coupled with mass spectrometry (LC-MS) analysis. Afterwards, 3D models of studied compounds were geometrically optimized using Density Functional Theory (DFT) molecular mechanics protocol in order to resemble real-state energies and structure of those derivatives. After calculating molecular descriptors from optimized 3D models, a matrix combining data from assays and molecular descriptors was created. Using Genetic Algorithm Multiple Linear Regression (GA-MLR) on this matrix allowed to create four mathematical models, one for each of the studied properties, which accurately predicts properties crucial in early drug-development stages. Such models can severely help in reducing costs of drug-development, by allowing exclusion of extremely unfavorable derivatives – even before the stage of synthesis.

COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2.

The COVID-19 has been creating a global crisis, causing countless deaths and unbearable panic. Despite the progress made in the development of the vaccine, there is an urge need for the discovery of antivirals that may better work at different stages of SARS-CoV-2 reproduction. The main protease (Mpro) of the SARS-CoV-2 is a crucial therapeutic target due to its critical function in virus replication. The α-ketoamide derivatives represent an important class of inhibitors against the Mpro of the SARS-CoV. While there is 99% sequence similarity between SARS-CoV and SARS-CoV-2 main proteases, anti-SARS-CoV compounds may have a huge demonstration's prospect of their effectiveness against the SARS-CoV-2. In this study, we applied various computational approaches to investigate the inhibition potency of novel designed α-ketoamide-based compounds. In this regard, a set of 21 α-ketoamides was employed to construct a QSAR model, using the genetic algorithm-multiple linear regression (GA-MLR), as well as a pharmacophore fit model. Based on the GA-MLR model, 713 new designed molecules were reduced to 150 promising hits, which were later subject to the established pharmacophore fit model. Among the 150 compounds, the best selected compounds (3 hits) with greater pharmacophore fit score were further studied via molecular docking, molecular dynamic simulations along with the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Our approach revealed that the three hit compounds could serve as potential inhibitors against the SARS-CoV-2 Mpro target.

A simple and robust model to predict the inhibitory activity of α-glucosidase inhibitors through combined QSAR modeling and molecular docking techniques.

Quantitative structure-activity relationships (QSAR) and molecular docking studies have been performed on a series of 35 α-glucosidase inhibitory derivatives. The QSAR models have been developed by genetic algorithm-multiple linear regression (GA-MLR) and least squares-support vector machine (LS-SVM) methods to correlate the conformational descriptors to the inhibitory activity. The obtained models with 5 descriptors were validated and illustrated to be statistically significant. They had desirable prediction based on squared correlation coefficient (R2), cross-validated correlation coefficient (Q2), root-mean-squares error (RMSE) and Fisher (F) parameters (R2 = 0.951, Q2 = 0.931, RMSE = 0.121, and F = 114.629 for GA-MLR model, and R2 = 0.989, Q2 = 0.987, RMSE = 0.056 and F = 543.754 for LS-SVM model). The crucial descriptor named DELS was explored to have the highest correlation with the inhibitory activity and thus has been chosen to build a simple model. The QSAR model developed with this mono-descriptor showed appropriate results of the predicted model using LS-SVM method (R2 = 0.888, Q2 = 0.872, RMSE = 0.185 and F = 221.459). Also, molecular docking which focuses on the interaction between ligands and α-glucosidase in the protein active site considered different binding positions to find the best binding mode. It helped the QSAR study to propose more comprehensive details of the compounds structures and was used to design more active compounds. The most active designed compound had a high inhibitory activity of 9.22 that can be proposed for the treatment of diabetes type 2.

In silico modeling of some HEPT analogues as anti -HIV agents using QSAR and molecular docking studies

Quantitative structure–activity relationship (QSAR), Density functional theory and molecular docking studies have been performed on a series of 49 1-[2-Hydroxyethoxy) methyl]-6-(phenylthio)-thymine (HEPT) derivatives as anti-HIV agents. A genetic algorithm multiple linear regression (GA-MLR) analysis has revealed that the five variable model comprises of 2D autocorrelation, 2D atom pairs and constitutional descriptors of these compounds are the governing factors for their biological activity. Using this GA-MLR model some new compounds have been proposed that have higher potency than the existing ones. The molecular docking study has shown that these new compounds can form hydrogen bonds with the receptor and have effective steric interactions.

Assessment through Statistical Methods of Water Quality Parameters(WQPs) in the Han River in Korea

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results (<TEX>$r^2=92.64$</TEX>,<TEX>$Q{^2}_{LOO}=91.45$</TEX>,<TEX>$Q{^2}_{Ext}=88.17$</TEX>). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

QSAR study of CK2 inhibitors by GA-MLR and GA-SVM methods

In this work, the quantitative structure–activity relationship models were developed for predicting activity of a series of compounds such as CK2 inhibitors using multiple linear regressions and support vector machine methods. The data set consisted of 48 compounds was divided into two subsets of training and test set, randomly. The most relevant molecular descriptors were selected using the genetic algorithm as a feature selection tool. The predictive ability of the models was evaluated using Y-randomization test, cross-validation and external test set. The genetic algorithm-multiple linear regression model with six selected molecular descriptors was obtained and showed high statistical parameters (R2train=0.893, R2test=0.921, Q2LOO=0.844, F=43.17, RMSE=0.287). Comparison of the results between GA-MLR and GA-SVM demonstrates that GA-SVM provided better results for the training set compounds; however, the predictive quality for both models is acceptable. The results suggest that atomic mass and polarizabilities and also number of heteroatom in molecules are the main independent factors contributing to the CK2 inhibition activity. The predicted results of this study can be used to design new and potent CK2 inhibitors.

Application of HS-SPME, SDME and Cold-Press Coupled to GC/MS to Analysis the Essential Oils of Citrus sinensis CV. Thomson Navel and QSRR Study for Prediction of Retention Indices by Stepwise and Genetic Algorithm-Multiple Linear Regression Approaches

Volatile fractions in the essential oils (EOs) obtained from the peels of Citrus sinensis CV. Thomson Navel by head-space solid phase micro-extraction (HS-SPME), single drop micro-extraction (SDME) and cold-press (CP) techniques were analyzed by means of gas chromatography-mass spectrometry (GC-MS). Fifteen, nine and fourteen compounds were identified by HS-SPME, SDME and CP methods, respectively representing 99.75 %, 99.45 % and 98.5 % of the chemical profiles in the peels of C. sinensis. The main components were found to be limonene (72.69 %, 71.96 %, 90.40 %), myrcene (9.65 %, 9.80 %, 1.10 %), sabinene (2.63 %, 9.35 %, 0.90 %) and α-pinene (2.00 %, 7.05 %, 1.60 %) by HS-SPME, SDME and CP, respectively. In addition, a quantitative structure-retention relationship (QSRR) study has been developed for prediction of retention indices (RIs) of the occurred natural compounds. The suitable set of molecular descriptors was calculated and the important descriptors were selected by stepwise (SW) and genetic algorithm (GA) variable selection features combined with multiple linear regression (MLR). A comparison between the attained results indicated the superiority of the genetic algorithm over the stepwise in the feature-selection step. The predictive quality of the QSRR models was tested for an external set of five compounds, randomly chosen out of 27 compounds. The genetic algorithm-multiple linear regression model (GA-MLR) with two selected descriptors gave rise in the best predicted results. The accuracy of the proposed GA-MLR model is illustrated using cross-validation, validation through an external test set and Y-randomization evaluation techniques.

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

In this work, a quantitative structure–activity relationship study was developed to predict the NaV1.7 antagonist activities. A data set consisted of 36 compounds with known NaV1.7 antagonist activities was split into two subsets of training set and test set using hierarchical clustering technique. To select the most respective descriptors among the pool of descriptors, genetic algorithm was applied. The model based on selected descriptors through genetic algorithm (GA) was built by employing multiple linear regression (MLR) method. The squared correlation coefficient (\( R_{\text{train}}^{2} \)) of 0.813, squared cross-validated correlation coefficient for leave-one-out (\( Q_{\text{LOO}}^{2} \)) of 0.699 and root mean square error of 0.214 were calculated for the training set compounds by GA–MLR model. The proposed model performed good predictive ability when it was verified by internal and external validation tests. The results of predictive model can lead to design better compounds with high NaV1.7 antagonist activities.

Prediction of tyrosinase inhibition for drug design using the genetic algorithm–multiple linear regressions

Modeling inhibition data for a series of most potent tyrosinase inhibitors for hyper-pigmentation treatment was done with quantitative structure–activity relationship (QSAR). In this report, multiple linear regression (MLR) methodology coupled with feature selection method, genetic algorithm (GA), was applied to derive QSAR models. A model with seven selected descriptors was obtained by the database that consisted of 49 compounds. The power of the model for prediction was verified with the leave-one-out and leave-group-out cross-validation test, which have values of 0.766 and 0.795, respectively. Moreover, the statistical parameters provided by the GA–MLR model (\( R_{\text{train}}^{ 2} \) = 0.85, \( R_{\text{test}}^{ 2} \) = 0.84, and \( F_{\text{train}}^{ 2} \) = 24.498) lead to a better understanding of the structural requirements of designing novel potent tyrosinase inhibitors. The study showed that the number of six-membered rings, the branching of a molecule, the van der Waals volume of atoms, the lopping centric index, the global charge transfer in a molecule and atomic masses are the main criteria in the inhibitory activity of compounds.

Quantitative Structure-Activity Relationship Study of Amino Acid Derivatives as Histone Deacetylase Inhibitors using the Genetic Algorithm – Multiple Linear Regression

The quantitative structure-activity relationship (QSAR) of the amino acid derivatives for prediction of histone deacetylase inhibitors activity was studied. Semi-empirical quantum chemical calculations at the AM1 level were used to find the 3D geometry of the studied molecules. The suitable set of the molecular descriptors was calculated, and the important descriptors were selected by the genetic algorithm (GA). The predictive quality of the QSAR model was tested for an external set of eight compounds, randomly chosen out of 38 compounds. The genetic algorithm-multiple linear regression (GA-MLR) model with six selected descriptors was obtained. The accuracy of the proposed GA-MLR model was illustrated using to following evaluation techniques: cross-validation, validation through an external test set, and Y-randomization. This model, demonstrating high statistical qualities .The predictive ability of the model was found to be satisfactory and could be used for predicting the histone deacetylase inhibitors activity of the molecules with low prediction error.

Exploring Novel QSRRs for Simulation of Gas Chromatographic Retention Indices of Diverse Sets of Terpenoids in Pistacia Lentiscus L. Essential Oil Using Stepwise and Genetic Algorithm Multiple Linear Regressions

The present work describes convenient and interpretable models by step-wise multiple linear regression (SW-MLR)and genetic algorithm- multiple linear regression (GA-MLR). These quantitative structure-retention relationship (QSRR) based strategies have been successfully used to predict the retention indices (RIs) of a series of natural compounds found in the essential oil of Pistacia lentiscus L. The dataset was divided into training set (51 compounds) and test set (25 compounds), randomly. The prediction capabilities of both approaches have been appraised by their exertion to test set compounds. The fitness statistics of the models were assessed to be satisfactory which resulted in accurate predictions. The best optimized models for SW-MLR and GA-MLR consisted 5 (X0, Qtot, Dv, HATS3e and MATS4e) and 6 (MATS2e, DELS, ATS4e, PW5, PCD and W) molecular descriptors, respectively. Our investigation revealed the superiority of SW-MLR (R2=0.96, and for training set; REP=3.7 for test set) standpoint against the best GA-MLR model (R2=0.955, for training set; REP=7.5 for test set) for estimation of RIs of similar or unknown compounds.

Quantitative structure–property relationship modeling of water‐to‐wet butyl acetate partition coefficient of 76 organic solutes using multiple linear regression and artificial neural network

The main aim of this study was the development of a quantitative structure-property relationship method using an artificial neural network (ANN) for predicting the water-to-wet butyl acetate partition coefficients of organic solutes. As a first step, a genetic algorithm-multiple linear regression model was developed; the descriptors appearing in this model were considered as inputs for the ANN. These descriptors are principal moment of inertia C (I(C)), area-weighted surface charge of hydrogen-bonding donor atoms (HACA-2), Kier and Hall index (order 2) ((2)χ), Balaban index (J), minimum bond order of a C atom (P(C)) and relative negative-charged SA (RNCS). Then a 6-4-1 neural network was generated for the prediction of water-to-wet butyl acetate partition coefficients of 76 organic solutes. By comparing the results obtained from multiple linear regression and ANN models, it can be seen that statistical parameters (Fisher ratio, correlation coefficient and standard error) of the ANN model are better than that regression model, which indicates that nonlinear model can simulate the relationship between the structural descriptors and the partition coefficients of the investigated molecules more accurately.

Quantitative Structure–Activity Relationship Study on the Anti‐HIV‐1 Activity of Novel 6‐Naphthylthio HEPT Analogs

The quantitative structure-activity relationship of the novel 6-naphthylthio 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio) thymine derivatives for prediction of anti-human immunodeficiency virus type 1 activity was studied. The suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections of the stepwise multiple linear regression and the genetic algorithm were selected. A comparison between the attained results indicated the superiority of the genetic algorithm over the stepwise multiple regression method in the feature-selection. The predictive quality of the quantitative structure-activity relationship models was tested for an external set of eight compounds, randomly chosen out of 39 compounds. The genetic algorithm-multiple linear regression model with six selected descriptors was obtained. This model, demonstrating high statistical qualities (R(2)(train) = 0.925, Q(2) = 0.872, SE (%) = 1.23, F = 49.338, R(2)(pred) = 0.944), could predict the anti-human immunodeficiency virus type 1 activity of the molecules with a prediction error percentage lower than 10%. The results suggest that electronegativity, the masses, and the atomic van der Waals volumes are the main independent factors contributing to the anti-human immunodeficiency virus type 1 activity of the studied compounds.

QSAR Study of 2‐(1‐Propylpiperidin‐4‐yl)‐1H‐Benzimidazole‐4‐Carboxamide as PARP Inhibitors for Treatment of Cancer

Quantitative structure-activity relationship of the 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide as a potent inhibitor of poly(ADP-ribose) polymerase for cancer treatment was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm was employed to select those descriptors that resulted in the best fitted models. Excellent results were obtained employing multiple linear regressions and critically discussed using a variety of statistical parameters. Furthermore, the model was validated using leave-one-out and leave-group-out cross-validation, external test set and chance correlation. A genetic algorithm-multiple linear regression model with seven selected descriptors was obtained. This model, with high statistical significance (R(2) = 0.935, Q(2)_(LOO)= 0.894, Q(2)_(LGO)= 0.875, F = 53.481), could be used to predict poly(ADP-ribose) polymerase inhibitor activity of the molecules.