Genetic Function Approximation Research Articles

Theoretical investigation and design of some indole derivatives as potent \u03b2-glucuronidase inhibitors

Backgroundβ-glucuronidase enzyme is mostly found in plants and animals. It plays a vital role in detoxification of reactive metabolites that are interrelated to several illnesses and the growth of colon cancer. It speeds up the breaking down of β-glucuronosyl-O-bonds. Lack of β-glucuronidase enzyme leads to Sly syndrome in humans, and overexpression of this enzyme leads to many diseases. Therefore, it becomes necessary to mediate the effect of this enzyme.ResultTheoretical investigation via QSAR modeling on 30 indole derivatives was performed to build a model which could be used to predict the activity of the indole derivatives. QSAR was carried out using multi-linear regression (MLR) method utilizing genetic function approximation (GFA) to develop the QSAR models. A very high predictive QSAR model was reported based on its statistical fitness with good internal and external validation parameters: R2trng = 0.954942, Qcv2 = 0.925462, R2test = 0.855393, and LOF = 0.042924. Molecular docking on the 30 indole derivatives was also performed to screen and identify the lead compound that would be used as template for designing new indole compounds. The docking investigation reveals that ligand 10 binds very tight in the binding pocket of β-glucuronidase enzyme with binding energy of − 9.5 kcal/mol. The ligand (10) was chosen as a template for designing new β-glucuronidase inhibitors. The four design compounds were found to be better than the template and the standard drug (D-saccharic acid 1, 4-lactone) with binding energies of − 9.6, − 9.7, − 9.8, and − 9.9 kcal/mol.ConclusionA very high predictive QSAR model with good internal and external validation parameters: R2trng = 0.954942, Qcv2 = 0.925462, R2test = 0.855393, and LOF = 0.042924, was built and reported in this study. Molecular docking investigation reveals that the most potent compound among all the data set was compound 10 with binding energy of − 9.5 kcal/mole. It bound to the binding pocket of β-glucuronidase enzyme via hydrophobic, electrostatic, and hydrogen bond, and it was retained as template for designing new indole compounds. The design compound with serial number ID 4 was identified to have the highest binding energy of − 9.9 kcal/mole among the designed compounds. It bound to the binding site of the β-glucuronidase enzyme via halogen, hydrophobic, electrostatic, and hydrogen bond. The design compounds were discovered to be better than the template used in the design and the standard drug.

Chemoinformatic studies on some inhibitors of dopamine transporter and the receptor targeting schizophrenia for developing novel antipsychotic agents

Chemoinformatic studies were carried on some inhibitors of dopamine transporter to develop a predictive and robust QSAR model and also to elucidate binding mode and molecular interactions between the ligands (inhibitors) and the receptor targeting schizophrenia as novel Antipsychotic agents. Density Functional Theory (DFT) approach was utilized to optimize the ligands at B3LYP/6-31G∗ at the ground state and Multi-linear regression of the genetic function approximation (MLR-GFA) method was employed in building Penta-parametric linear equation models. The best model with statistically significant parameters has squared correlation coefficient R2= 0.802, adjusted squared correlation coefficient R2adj = 0.767, Leave one out (LOO) cross-validation coefficient (Q2) = 0.693, lack of fit score (LOF) = 0.406, R2Test = 0.77, Y-randomization test (cR2p) = 0.714, Chi-squared (χ2) =0.026, bootstrapping (Systematic errors = 0.272) and Variance Inflation Factor (VIF) <2 . The obtained results were compared with standard validation parameters to ascertain the predictivity, reliability, and robustness of the model. Also, the mechanistic interpretation of the descriptors found in the model revealed that two out of five descriptors; MATS7s (32.3%) and RDF95m (30.4%) having pronounced influence on the observed antipsychotic property of the compounds evidenced by their highest percentage contributions. More so, the molecular docking investigation showed that the binding affinity of the selected ligands ranges from -10.05 to -9.0 kcal/mol and with ligand 21 possessed the highest binding affinity (-10.05 kcal/mol). Furthermore, all the selected ligands displayed hydrogen bonds and hydrophobic interactions with the amino acid residues of the target (4M48) which could account for their higher binding energy. Our findings revealed that the developed model passed the general requirements for an acceptable QSAR model and also satisfied the OECD principles for model development. Hence, the developed model would be practically useful as a blueprint in developing novel antipsychotic agents with improved activity for the treatment of schizophrenia mental disorder.

Quantitative Structure and activity Relationship of 3a, 6a – Dihydro-1H- pyrrolo[ 3,4-c] pyrazole-4,6-dione Derivatives as anti HIV-1 Agents

A novel series of 3a, 6a – Dihydro-1H- pyrrolo[ 3,4-c] pyrazole-4,6-dione Derivatives have been reported as better anti-HIV 1 agents. In this study QSAR was carried on a 3a, 6a – Dihydro-1H- pyrrolo[ 3,4-c] pyrazole-4,6-dione Derivatives as anti HIV – 1 agents. Two different variable selection approaches namely: Genetic function approximation and multi linear regression models were used to predict the HIV-1 inhibition activity. The following were obtained after the model was internally validated: squared correlation coefficient (R2) of 0.8823, adjusted squared correlation coefficient (R2adj) of 0.8528 and leave one out (LOO) cross validation coefficient (Q2cv) of 0.7566. The external validation was carried out to confirm the predictive power of the model and R2pred of 0.6901 was obtained. The validated model result above showed that the five descriptors which are GATS6c, VR3_Dze, minHCsats, RDF30m and Eze contributed positively to the activity. The result obtained will be very helpful for designing and synthesizing other derivatives with improved anti-HIV activities.

Quantitative structure-activity relationship study for prediction of antifungal properties of phenolic compounds

Antifungal compounds are of interest to reduce commodity spoilage and exposure to mycotoxins. In this study, a series of quantitative structure-activity relationship (QSAR) equations based on topological properties were developed to gain insight into the antifungal activities of phenolic compounds. The molecules were geometry optimized using B3LYP/6-311+G** density functional theory calculations. Analysis of the frontier orbital properties revealed that conjugated phenolic compounds possessed smaller band gap energies. Genetic function approximation (GFA) on populations of 100 one to two descriptor models over 10,000 generations identified several models for antifungal activity against Fusarium verticillioides, Fusarium oxysporum, Aspergillus flavus, Aspergillus fumigatus, Penicillium expansum, and Penicillium brevicompactum. Phenolic compounds with greater antifungal activity possessed a lower electrophilicity index. The correlation coefficients for the one and two descriptor models ranged from 0.627 to 0.790 and 0.762 to 0.939, respectively. Molecular descriptors associated with electrostatic and topological properties are important to describe the antifungal activities of the phenolic compounds studied.

Quantitative structure-activity relationship (QSAR) and design of novel ligands that demonstrate high potency and target selectivity as protein tyrosine phosphatase 1B (PTP 1B) inhibitors as an effective strategy used to model anti-diabetic agents

Diabetes and obesity have increased dramatically in recent decades worldwide. Diabetes mainly emerged as a major health care burden disease in both the US and other industrialized countries, among which type II diabetes is the most common. Discovering new and effective treatments for diabetes is currently a high international health priority. In the present study a computational technique was used to model 97 compounds with PTP-1B inhibitory activity, in order to demonstrate the Quantitative structure-activity relationship (QSAR) of these compounds a genetic function approximation (GFA) algorithm was applied to pick the best descriptors and multiple linear regression (MLR) was used to establish a relationship between the PTP-1B inhibitory activity of these compounds and the best molecular descriptors. This QSAR study allowed investigating the influence of very simple and easy-to-compute descriptors in determining biological activities, which shed light on the key factors that aid in the design of novel potent molecules using computer-aided drug design tools.

QSAR and molecular docking studies of novel 2,5-distributed-1,3,4-thiadiazole derivatives containing 5-phenyl-2-furan as fungicides against Phythophthora infestans

BackgroundThe 1,3,4-thiadiazoles are among the structural moieties that were found to be of utmost importance in the fields of pharmacy and agrochemicals because of their widespread biological activity that includes anti-tumor, antibacterial, anti-inflammatory, antihypertensive, anti-tuberculosis, anticonvulsant, and antimicrobial, among others.ResultsQSAR and molecular docking studies were carried out on thirty-two (32) derivatives of 2,5-disubstituted-1,3,4-thiadiazoles for their antifungal activities toward Phytophthora infestans. Using the “graphical user interface” of Spartan14 software, the structure of the compounds of the dataset is drawn and then optimized at DFT/B3LYP/6-31G* quantum mechanical method of the software. Molecular descriptors of the optimized compounds were calculated and later on divided into the training set and test sets (at a ratio of 3:1). The training set was used for model generation and the test set was for external validation of the generated model. Four models were generated by the employment of genetic function approximation (GFA) in which the optimal model (4) turned out to have the following statistical parameters: R2 = 0.798318, R2adj = 0.750864, cross-validation R2(Q2cv) = 0.662654, and external validation R2pred = 0.624008. On the molecular docking study of thiadiazole compounds with the target protein of Phytophthora infestans effector site (PDB ID: 2NAR ), compound 13 shows the highest binding affinity with − 9.3 kcal/mol docking score and composes hydrophobic as well as H-bond interactions with the target protein (2NAR).ConclusionThe result of the QSAR study signifies the stability and robustness of the built model by considering the validation parameters and this gave an idea of template/ligand-based design while the molecular docking study revealed the binding interaction between the ligand and the protein site which gave an insight toward an “optimization method” of the structure-based design for the discovery of more potent compounds with better activity against Phytophthora infestans using the approach of computer-aided drug design (CADD) in plant pathology.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method.

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure–property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

QSAR, molecular docking, and design of novel 4-(N,N-diarylmethyl amines) Furan-2(5H)-one derivatives as insecticides against Aphis craccivora

BackgroundAphis craccivora has many plant hosts, though it seemingly forechoice to groups of bean family. Other plants it hosts are families of Solanaceae, Rosaceae, Malvaceae, Chenopodiaceae, Caryophyllaceae, Ranunculaceae, Cucurbitaceae, Brassicaceae, and Asteraceae.ResultA computational study was carried out on a series of twenty compounds of novel 4-(N,N-diarylmethylamines) furan-2(5H)-one derivatives against Aphis craccivora insect. Optimization of the compounds was performed with the aid of Spartan 14 software using DFT/B3LYP/6-31G** quantum mechanical method. Using PaDel descriptor software to calculate the descriptors, Generic Function Approximation (GFA) was employed to generate the model. Model 1 found to be the optimal out of four models generated which has the following statistical parameters; R2 = 0.871489, R2adj = 0.83644, cross-validated R2 = 0.790821, and external R2 = 0.550768. Molecular docking study occurred between the compounds and the complex crystal structure of the acetylcholine (protein AChBP) (PDB CODE 2zju) in which compound 13 was identified to have the highest binding energy of − 8.4 kcalmol−1. Statistical analyses, such as variance inflation factor, mean effect, and the applicability domain, were conducted on the model. This compound has a strong affinity with the macromolecular target point of the A. craccivora (2zju) producing H-bond and as well the hydrophobic interaction at the target point of amino acid residue. Molecular docking gave an insight into the structure-based design of the new compounds with better activity against A. craccivora in which three compounds A, B, and C were designed and discovered to be of high quality and have greater binding affinity compared to the one obtained from the literature.ConclusionThe QSAR model was generated by the employment of Genetic Function Approximation (GFA). The model was found to be robust and possessed a good statistical parameter. Furthermore, a molecular docking study was performed to get an idea for structure-based design in which three (3) compounds A, B, and C were designed and were found to be more active than the template (compound 13, i.e., the one with highest docking score). QSAR model was developed to give an insight into the ligand/template-based design of computer-aided drug design.

Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors

QSAR modelling on Thirty (34) novel quinazoline derivatives (EGFRWT inhibitors) as non-small cell lung cancer (NSCLC) agents was performed to develop a model with good predictive power that can predict the activities of newly designed compounds that have not been synthesised. The EGFRWT inhibitors were optimized at B3LYP/6-31G* level of theory using Density Functional Theory (DFT) method. Multi-Linear Regression using Genetic Function Approximation (GFA) method was adopted in building the models. The best one among the models built was selected and reported because it was found to have passed the minimum requirement for the assessment of QSAR models with the following assessment parameters: R2 of 0.965901, R2adj of 0.893733, Qcv2 of 0.940744, R2test of 0.818991 and LOF of 0.076739. The high predicted power, reliability, robustness of the reported model was verified further by subjecting it to other assessments such VIF, Y-scrambling test and applicability domain. Molecular docking was also employed to elucidate the binding mode of some selected EGFRWT inhibitors against EGFR receptor (4ZAU) and found that molecule 17 have the highest binding affinity of -9.5 kcal/mol. It was observed that the ligand interacted with the receptor via hydrogen bond, hydrophobic bond, halogen bond, electrostatic bond and others which might me the reason why it has the highest binding affinity. Also, the ADME properties of these selected molecules were predicted and only one molecule (34) was found not orally bioavailable because it violated more than the permissible limit set by Lipinski's rule of five filters. This findings proposed a guidance for designing new potents EGFRWT inhibitors against their target enzyme.

Multi-linear regression model, molecular binding interactions and ligand-based design of some prominent compounds against Mycobacterium tuberculosis

Multidrug-resistant strains of Mycobacterium tuberculosis still remain a major challenge in creating first-hand drugs for treating tuberculosis. Development and syntheses of novel compounds with more potent anti-tubercular agents are usually by the trial and error approach which is time consuming and expensive. QSAR is a theoretical approach which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. Tuberculosis. This approach was employed to develop the multivariate QSAR model to correlate the chemical structures of the 1,2,4-triazole analogues with their observed activities using a theoretical approach. To build the robust QSAR model, genetic function approximation was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced with molecular descriptors, AATS7s, nHBint3, minHCsatu, TDB9e, RDF90i and RDF110s which have been validated through internal and external validation tests. Molecular docking studies were also carried for all the studied compounds to show the interactions and binding modes between the ligand and the receptor (DNA gyrase). The lead compound (compound 34) with higher anti-tubercular activity was observed with prominent binding affinity of 21.9 kcal/mol compared to the recommended drugs: isoniazid (− 14.6 kcal/mol) and ethambutol (− 5.8 kcal/mol). Therefore, compound 34 served as a template structure to designed compounds with more efficient activities. Among the compounds designed, compounds 34p, 34q and 34r were observed with better anti-tubercular activities with more prominent binding affinities which ranges from − 24.3 to − 28.6 kcal/mol. The outcome of this research is recommended for pharmaceutical and medicinal chemists to synthesize and carry out an in vivo and in vitro screening for the proposed designed compounds to substantiate the computational findings.

IN SILICO ELUCIDATION OF SOME QUINOLINE DERIVATIVES WITH POTENT ANTI-BREAST CANCER ACTIVITIES.

Abstract: The toxicity and high resistance to the commercially sold breast-cancer drugs have become more alarming and the demand to produce new and less toxic breast-cancer drugs arises. In silico studies was carried out on some quinoline derivatives to investigate their reported activities against breast cancer and thereby generate a model with a better activity against breast cancer. The chemical structures of the compounds were optimized using Spartan software at Density Functional Theory (DFT) level, utilizing the B3LYP/ 6-31G* basis set. Four QSAR models were generated using Multi-Linear Regression (MLR) and Genetic Function Approximation (GFA) method. Equation one was chosen as the best model based on the validation parameters. The validation parameters was found to be statistically significant with square correlation coefficient (R2) of 0.9853, adjusted square correlation coefficient ( ) of 0.9816, cross validation coefficient ( ) of 0.9727 and an external correlation coefficient square ( ) of 0.6649 was used to validate the model. The built model was a good and robust one for it passed the minimum requirement for generating a QSAR model.

Genetic functional algorithm model, docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis

ABSTRACT Tuberculosis still persist a major challenge to healthcare system around the world. Increasing rates of morbidity, mortality and the reoccurrence of the resistant strains of the disease to accessible drugs/medications have made it crucial to search for a new therapy in term of novel and prominent hypothetical compound treat tuberculosis. Therefore, Quantitative Structure-Activity Relationship (QSAR) was employed as a modelling technique to predict and design new compounds with improved and efficient biological activities against Mycobacterium tuberculosis. To accomplish the purpose of this work, Genetic function approximation was adopted to derive the model. The established model was swayed with topological descriptors; MATS9s, SM1_DzZ, SpMin4_Bhv, TDB2v and RDF70v.More also, interactions between the compounds and the target protein ‘‘DNA gyrase’’ indicated that compound 20 has the most perceptible binding affinity of -18.12kcal/mol. Consequently, compound 20 served as a reference structural template and insight to design fourteen novel hypothetical agents with more prominent anti-tubercular activities. More also, compound 20j was observed with high activity among the designed compounds with more a prominent binding affinities of -24.3 kcal/mol. Therefore this research recommends in-vivo, in-vitro screening and pharmacokinetic properties to be carried out in order to determine the toxicity of the designed compounds.

A robust model for estimating thermal conductivity of liquid alkyl halides

ABSTRACTThermal conductivity is an essential thermodynamic property in chemical engineering application. As a result, estimating the thermal conductivity of organic compounds is of significance in industry production. Alkyl halides are important organic intermediates and raw materials, but little investigations have been performed to estimate their thermal conductivity. In this study, the structures of compounds were optimized in Gaussian 09W and molecular descriptors were extracted by Dragon software. Finally, we developed a 6-descriptor linear quantitative structure–property relationship (QSPR) model to estimate the thermal conductivity of alkyl halides using the genetic function approximation (GFA) method. Validation proved that the developed model had goodness-of-fit, robustness and predictive ability. The r2pred and root-mean-square error (RMSEP) of prediction set for the model were equal to 0.9745 and 0.0035, respectively. Meanwhile, the applicability domain was visualized by means of the Williams plot. This study provides a new model for estimating the thermal conductivity of this important class of chemicals.

Four Methods to Estimate Minimum Miscibility Pressure of CO2‐Oil Based on Machine Learning

Summary of main observation and conclusionCO2 flooding accounts for a considerable proportion in gas flooding. Using CO2 as a gas displacement agent is benefit for enhanced oil recovery (EOR), and the alleviation of the greenhouse effect by the permanent storage of CO2 in the crust. Minimum miscibility pressure (MMP) of CO2‐oil is a key factor affecting EOR, which determines the yield and economic benefit of crude oil recovery. Therefore, it is of great importance to use fast, accurate and cheap prediction methods for MMP estimation. In the present study, to evaluate the reliability of four recently developed prediction models based on machine learning (i.e., neural network analysis (NNA), genetic function approximation (GFA), multiple linear regression (MLR), partial least squares (PLS)), 136 sets of data are selected for calculation via outlier analysis from 147 sets of data. Afterwards, we compared the four models with existing prediction models from the literature. The analysis of correlation coefficients and multiple error functions shows that the four models can solve the MMP prediction problem well, and the model using intelligent algorithm has a higher prediction accuracy than the simple linear model. Besides, intelligent methods based on similarity algorithm have little difference from each other. Finally, a sensitivity analysis was conducted.

Quantitative structure-activity relationship between the toxicity of amine surfactant and its molecular structure

With the extensive applications and ongoing world demand, more and more amine surfactants are discharged into natural environment. However, the database about toxicity of amine surfactants is incomplete, which is not beneficial to environmental protection process. In this paper, the toxicity of 20 amine surfactants on Daphnia magna were tested to extend the toxicity data of amine surfactants. Besides, 35 molecular structure descriptors including quantum parameters, physicochemical parameters and topological indices were chosen and calculated as independent variables to develop the quantitative structure-activity relationship (QSAR) model between the toxicity of amine surfactants and their molecular structure by genetic function approximation (GFA) algorithm. According to statistical analysis, a robust model was built with the determination coefficient of (R2) was 0.962 and coefficient determinations of cross-validation (Rcv2) was 0.794. Meanwhile, external validation was implemented to evaluate the QSAR model. The result of coefficient determinations of cross-validation (Rext2) for external validation was calculated as 0.942, illustrating the model has great goodness-of-fit and good prediction ability.

Quantitative structure property relationship for relative volatility of isopropanol and water mixture

ABSTRACT Extractive distillation is a common method to separate the mixture, which has the low relative volatility. Many solvents were screened to separate the mixture based on the experiment. Because the number of candidate solvents is high and the experiment is time-consuming. Quantitative structure property relationship was introduced to model and evaluate the relative volatility of isopropanol and water mixture. The model was developed based on genetic function approximation and 10 molecular descriptors were involved in using Material Studio software. The result indicated that the built model was sufficient for predicting the suitable solvent for isopropanol and water mixture.

Synthesis, anti-HIV activity, molecular modeling study and QSAR of new designed 2-(2-arylidenehydrazinyl)-4-arylthiazoles

Taking into consideration the eminence of 1,3-thiazoles in medicinal chemistry and in a view of procuring more pronounced biological contour, the synthesis of 2-(2-arylidenehydrazinyl)-4-arylthiazoles 6–43 was made possible by the cyclization reaction of thiosemicarbazones and α-bromoacetophenones. The thiosemicarbazones 5a-m were in turn synthesized from substituted benzaldehydes or acetophenones and thiosemicarbazide. Optimization of the reaction conditions was carried out in order to attain the target molecules in good yields. All the new compounds were evaluated in vitro for their antiviral activity against the replication of HIV-1 and HIV-2 in MT4 cells using a MTT assay. Screening results indicated that compounds 32–34 are the only compounds in the series inhibiting HIV-1 and HIV-2 replication in cell cultures with IC50 of >2.71, >2.19 and > 1.71 μM, respectively. The molecular docking of compounds 32 and 34 with some amino acids of human immunodeficiency virus reverse transcriptase (HIV RT) were also studied. The preliminary quantum structure-activity relationship (QSAR) among the newly synthesized congeners was obtained by two methods, Multiple Linear Regression (MRL) and Genetic Function Approximation (GFA).

Quantitative Relationship between CO2 Absorption Capacity and Amine Water System: DFT, Statistical, and Experimental Study

CO2 emissions reduction has become a hot issue to relieve global warming in recent years. CO2 absorption with amines aqueous solutions is a promising method for its capture, and increasing CO2 capacity and reducing energy consumption are essential for its application. In this work, a data set of 29 different amines was regressed with genetic function approximation (GFA) and artificial neural network (ANN) algorithm to find a predictive quantitative relationship between amine structure and CO2 absorption capacity. Density functional theory (DFT) methods at levels B3LYP and 6-311+g (d,p) were used to optimize the structures. Both reactants and products were introduced in regression, and an optimal model with best relevance and predictability for CO2 absorption at 313 K was obtained and experimentally confirmed. Descriptors analysis showed that reducing the number of hydroxyl groups, increasing molecular mass, and changing steric effect of amine structure would increase its CO2 absorption capacity. Two new a...

Quantitative structure-retention relationships of the chromatographic retentions of phthalic acid ester contaminants in foods.

The harmful health effects caused by phthalic acid esters have been supported from the increasing scientific evidence, developing the efficient methodologies to monitor the levels of phthalic acid esters in various foods become especially important from the aspects of human exposure assessment and their migration mechanistic understanding. In this study, quantitative structure-retention relationship studies on both the gas and liquid chromatographic retention times of 23 phthalic acid esters were performed by genetic function approximation, and the optimal quantitative structure-retention relationship models (r2 >0.980, r2 CV >0.960, and r2 pred >0.865) passed the statistical tests of cross-validation, randomization, external prediction, Roy' rm 2 metrics, Golbraikh-Tropsha' criteria and applicability domain. The established predictive models elucidate the structural requirements for the retention of phthalic acid esters over different chromatographic columns, which were finally used to predict the retention times of 11 new phthalic acid esters. Hopefully, this work could provide useful guidelines for better understanding and accurate prediction of the retention behavior of undetermined phthalic acid esters when lacking standard samples or under poor experimental conditions, and make the simultaneous identification and quantification of numerous phthalic acid esters possible.

A Derived QSAR Model for Predicting Some Compounds as Potent Antagonist against Mycobacterium tuberculosis: A Theoretical Approach.

Development of more potent antituberculosis agents is as a result of emergence of multidrug resistant strains of M. tuberculosis. Novel compounds are usually synthesized by trial approach with a lot of errors, which is time consuming and expensive. QSAR is a theoretical approach, which has the potential to reduce the aforementioned problem in discovering new potent drugs against M. tuberculosis. This approach was employed to develop multivariate QSAR model to correlate the chemical structures of the 2,4-disubstituted quinoline analogues with their observed activities using a theoretical approach. In order to build the robust QSAR model, Genetic Function Approximation (GFA) was employed as a tool for selecting the best descriptors that could efficiently predict the activities of the inhibitory agents. The developed model was influenced by molecular descriptors: AATS5e, VR1_Dzs, SpMin7_Bhe, TDB9e, and RDF110s. The internal validation test for the derived model was found to have correlation coefficient (R2) of 0.9265, adjusted correlation coefficient (R2 adj) value of 0.9045, and leave-one-out cross-validation coefficient (Q_cv∧2) value of 0.8512, while the external validation test was found to have (R2 test) of 0.8034 and Y-randomization coefficient (cR_p∧2) of 0.6633. The proposed QSAR model provides a valuable approach for modification of the lead compound and design and synthesis of more potent antitubercular agents.