The results obtained in this study are in good agreement with the experimental data presented in the literature. The model for training and test sets attained by the optimum 12 parameters gave highly satisfactory results with R2 training= 0.889, q2=0.839 and SEtraining=0.066, q2 ext1 = 0.770, q2 ext2 = 0.750, q2 ext3=0.824, ccctr = 0.941, ccctest = 0.869 and cccall = 0.927.
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.
Since there is no 4D-QSAR research on metal based organic complexes in the literature, this study is original and gives a powerful tool to the design of novel and selective ruthenium(II) arene complexes.
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.
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