Quantitative bioactivity prediction and pharmacophore identification for benzotriazine derivatives using the electron conformational-genetic algorithm in QSAR

Combined 4D-QSAR and target-based approaches for the determination of bioactive Isatin derivatives

The hybrid method of the Electron-Conformational Genetic Algorithm (EC-GA) was used to determine the pharmacophore groups and to estimate anticancer activity in isatin derivatives using a robust 4D-QSAR software (EMRE). To build the model, each compound is represented by a set of conformers rather than a single conformation. The Electron Conformational Matrix of Congruity (ECMC) is composed via EMRE software. Electron Conformational Submatrix of Activity (ECSA) was calculated by the comparison of these matrices. Genetic algorithm was used to select important variables to predict theoretical activity. The model with the best seven parameters produced satisfactory results. The E statistics technique was applied to the generated EC-GA model to evaluate the individual contribution of each of the descriptors on biological activity. The r² and q² values of the training set compounds were found to be 0.95 and 0.93, respectively. Because no previous 4D-QSAR studies on isatin derivatives have been conducted, this study is important in the development of new isatin derivatives. In this study, 27 isatin derivatives whose activities were estimated using the hybrid EC-GA method were also investigated through molecular docking and molecular dynamics simulations for their BCL-2 inhibitory activity.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17β-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q² = 0.650) with good external set prediction was obtained.

The effect of stereoisomerism on the 4D-QSAR study of some dipeptidyl boron derivatives

The electron conformational genetic algorithm (EC-GA) method had been employed by distinguishing between enantiomers for the first time as a 4D-QSAR approach to reveal the pharmacophore (Pha) and to predict the bioactivity of the dipeptidyl boron compounds. The Electron Conformational Matrices of Congruity (ECMCs) were prepared for all conformers of compounds in the data set based on the quantum chemical calculations at HF/3-21 G level in an aqueous medium. The comparison of the ECMCs within the certain tolerances by the EMRE program revealed the pharmacophore for some dipeptidyl boron derivatives. For the selection of the most influential parameters on the activity and the calculation of theoretical activities, the genetic algorithm with the non-linear least square method was used. The final model was validated by the cross-validation method with the division of the data set into training and test items. The 12-parameter model gave excellent statistical results (R²_training = 0.850, R²_test = 0.809, q² = 0.755, q²_ext1 = 0.776, q²_ext2 = 0.759, q²_ext3 = 0.735, CCC_tr = 0.922, CCC_test = 0.846, CCC_all = 0.905). Because of the inexistence of 4D-QSAR studies on the dipeptidyl boron derivatives and the stereoisomerism effect on the biological activity was examined for the first time for these compounds, this study plays an important role in the development of new boron-containing compounds.

DNA Cleavage Properties, Antimicrobial and Cytotoxic Activity and 4D-QSAR Analysis of Some Pyrazole Derivatives

Background:

An extensive study of 19 pyrazole derivatives were carried out based on the evaluation of DNA cleavage properties, antimicrobial and cytotoxic activities and 4D-QSAR analysis including pharmacophore modelling and bioactivity prediction by the Electron Conformational-Genetic Algorithm (EC-GA) method.

Methods:

The pyrazole derivatives were tested for their antimicrobial activity against certain human pathogenic organisms using the agar diffusion procedure. Binding of compounds with DNA was studied by gel electrophoresis using plasmid pBR322 DNA. The compounds were investigated for their properties as cytotoxic agents by brine shrimp lethality bioassay. To identify the pharmacophoric elements and find out the most important molecular properties which govern cytotoxic activity, multiple conformations of the compounds were used.

Results:

The urea derivatives of pyrazole had higher antibacterial activities against Gram-negative bacteria than against Gram-positive bacteria. Many of the compounds were found to cleave plasmid pBR322 DNA from the supercoiled form to the nicked circular. The cytotoxicity values of the compounds ranged from 13.87 to 84.1 µg/mL. The generated QSAR model was evaluated through the use of the Leave-One-Out Cross Validation (LOO-CV) method. A statistically significant and considerably predictive QSAR model was obtained with 4- descriptors resulting in R2 training =0.8223, R2 test =0.9346, q2=0.6201, q2 ext1=0.8672, q2 ext2= 0.8662 and q2 ext3=0.9511.

Discussion:

The generated model demonstrates that geometrical parameters are more correlated with cytotoxic activity. The resulting EC-GA model would provide benefits to design novel bioactive pyrazole derivatives which are more potent and have less side effects.

Conclusion:

It is believed that the generated QSAR model gives insight into developing new more potent pyrazole derivative drugs.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

4D-QSAR investigation and pharmacophore identification of pyrrolo[2,1-c][1,4]benzodiazepines using electron conformational-genetic algorithm method

In this paper, we present the results of pharmacophore identification and bioactivity prediction for pyrrolo[2,1-c][1,4]benzodiazepine derivatives using the electron conformational-genetic algorithm (EC-GA) method as 4D-QSAR analysis. Using the data obtained from quantum chemical calculations at PM3/HF level, the electron conformational matrices of congruity (ECMC) were constructed by EMRE software. The ECMC of the lowest energy conformer of the compound with the highest activity was chosen as the template and compared with the ECMCs of the lowest energy conformer of the other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA, i.e. pharmacophore) by ECSP software. A descriptor pool was generated taking into account the obtained pharmacophore. To predict the theoretical activity and select the best subset of variables affecting bioactivities, the nonlinear least square regression method and genetic algorithm were performed. For four types of activity including the GI50, TGI, LC50 and IC50 of the pyrrolo[2,1-c][1,4] benzodiazepine series, the r(2)train, r(2)test and q(2) values were 0.858, 0.810, 0.771; 0.853, 0.848, 0.787; 0.703, 0.787, 0.600; and 0.776, 0.722, 0.687, respectively.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

4D-QSAR study of HEPT derivatives by electron conformational-genetic algorithm method

In this work, the EC-GA method, a hybrid 4D-QSAR approach that combines the electron conformational (EC) and genetic algorithm optimization (GA) methods, was applied in order to explain pharmacophore (Pha) and predict anti-HIV-1 activity by studying 115 compounds in the class of 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) derivatives as non-nucleoside reverse transcriptase inhibitors (NNRTIs). The series of NNRTIs were partitioned into four training and test sets from which corresponding quantitative structure-activity relationship (QSAR) models were constructed. Analysis of the four QSAR models suggests that the three models generated from the training and test sets used in previous works yielded comparable results with those of previous studies. Model 4, the data set of which was partitioned randomly into two training and test sets with 11 descriptors, including electronical and geometrical parameters, showed good statistics both in the regression (r2(training) )= 0.867, r2test = 0.923) and cross-validation (q (2) = 0.811, q2(ext1) = 0.909, q2(ext2) = 0.909) for the training set of 80 compounds and the test set of 27 compounds. The prediction of the anti-HIV-1 activity of HEPT compounds by means of the EC-GA method allowed for a quantitatively consistent QSAR model. In addition, eight novel compounds never tested experimentally have been designed theoretically using model 4.

Conformation depends on 4D-QSAR analysis using EC-GA method: pharmacophore identification and bioactivity prediction of TIBOs as non-nucleoside reverse transcriptase inhibitors

The electron conformational and genetic algorithm methods (EC-GA) were integrated for the identification of the pharmacophore group and predicting the anti HIV-1 activity of tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives. To reveal the pharmacophore group, each conformation of all compounds was arranged by electron conformational matrices of congruity. Multiple comparisons of these matrices, within given tolerances for high active and low active TIBO derivatives, allow the identification of the pharmacophore group that refers to the electron conformational submatrix of activity. The effects of conformations, internal and external validation were investigated by four different models based on an ensemble of conformers and a single conformer, both with and without a test set. Model 1 using an ensemble of conformers for the training (39 compounds) and test sets (13 compounds), obtained by the optimum seven parameters, gave satisfactory results (R²(training) = 0.878, R²(test)= 0.910, q² = 0.840, q²(ext1) = 0.926 and q²(ext2) = 0.900).