Application of electron conformational-genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction

Gecen N., SARIPINAR E., Yanmaz E., Sahin K.

JOURNAL OF MOLECULAR MODELING, cilt.18, sa.1, ss.65-82, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18 Sayı: 1
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s00894-011-1024-5
  • Dergi Adı: JOURNAL OF MOLECULAR MODELING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.65-82
  • Anahtar Kelimeler: Dihydropyridines, Drug design, Electron conformational-genetic algorithm, Pharmacophore, 4D-QSAR, COMPONENT ANALYSIS PCA, NEURAL-NETWORKS, QSAR MODELS, ELECTROPHILICITY, CLASSIFICATION, DESCRIPTORS, DERIVATIVES, LIGANDS, INDEXES, ANALOGS
  • Erciyes Üniversitesi Adresli: Evet

Özet

Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)-which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements-were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (R-training(2) = 0.848, R-test(2) = 0.904, with a cross-validated q(2) = 0.780).