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
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.
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Electron conformational–genetic algorithm;
electron conformational method;
Document Type: Research Article
Department of Chemistry, Science Faculty, Erciyes University, Kayseri, Turkey
Department of Chemistry, Science and Arts Faculty, Siirt University, Siirt, Turkey
Information Technology Department, Abdullah Gül University, Kayseri, Turkey
Publication date: April 2, 2016