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

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.

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.

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.