The use of the Klopman index as a new descriptor for pharmacophore analysis on strong aromatase inhibitor flavonoids against estrogen-dependent breast cancer


Kizilcan D. S., TÜRKMENOĞLU B., GÜZEL Y.

STRUCTURAL CHEMISTRY, vol.31, no.4, pp.1339-1351, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1007/s11224-020-01498-9
  • Journal Name: STRUCTURAL CHEMISTRY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chimica, INSPEC
  • Page Numbers: pp.1339-1351
  • Keywords: Flavonoid derivatives, 4D-QSAR, MCET, Pharmacophore, MOLECULAR DOCKING, PHYTOESTROGENS, IDENTIFICATION, BINDING, PREDICT
  • Erciyes University Affiliated: Yes

Abstract

We present the Klopman index, which is effective in explaining the mechanisms of organic chemistry, in quantitative structure activity relationship (QSAR) studies on flavonoids as a new descriptor for the first time. Instead of using random descriptors to elucidate the interactions between the ligand and the receptor, the atomic local reactive descriptor (LRD) which we call as the Klopman index in our molecular conformer electron topological (MCET) program was used. Both the atomic charges and the frontier orbital coefficients are used simultaneously in the Klopman index. Substrate-aromatase interactions of strong aromatase inhibitors against estrogen-dependent breast cancer were investigated by the pharmacophore model (PhaM) defined using MCET. All the conformers in the series were aligned with the template to match with the enzyme in three dimensional (3D). The parameters of the enzyme at the interaction points were determined according to the descriptor of the molecules in the training set. Contour maps of enzyme parameters show the interaction points of aromatase with the flavonoid group used in this study. Traditional validation parameters (Q(2) = 0.883 and R-pred(2) = 0.895) were supported by a more stringent validation (a new parameter R-m(2)-overall = 0.6).