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

Özalp A., YAVUZ S., Sabancı N., Çopur F., KÖKBUDAK Z., SARIPINAR E.

SAR and QSAR in Environmental Research, cilt.27, sa.4, ss.317-342, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 4
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1080/1062936x.2016.1174152
  • Dergi Adı: SAR and QSAR in Environmental Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.317-342
  • Anahtar Kelimeler: 4D-QSAR, pharmacophore, electron conformational method, Electron conformational–genetic algorithm, genetic algorithm, pyrrolo[2,1-c][1,4]benzodiazepines
  • Erzincan Binali Yıldırım Üniversitesi Adresli: Hayır

Özet

© 2016 Informa UK Limited, trading as Taylor & Francis Group.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 r2 train, r2 test and q2 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.