TITLE:
In Silico Docking of Rhodanine Derivatives and 3D-QSAR Study to Identify Potent Prostate Cancer Inhibitors
AUTHORS:
Koffi Alexis Respect Kouassi, Adenidji Ganiyou, Diomande Gbe Gondo Didier, Anoubilé Benié, Ziao Nahossé
KEYWORDS:
Rhodanine, Pharmacophores, 3D-QSAR, Molecular Docking
JOURNAL NAME:
Computational Chemistry,
Vol.10 No.2,
February
18,
2022
ABSTRACT: The 3VHE protein is considered as a potential target for the treatment of prostate cancer. In order to find new 3VHE inhibitors, pharmacophore models based on the molecular structure of rhodanine derivatives and a three-dimensional quantitative structure-activity relationship model (3D-QSAR) have been developed and validated by different methods. The 3D-QSAR model was evaluated for its predictive performance on a diverse test set containing 18 prostate cancer inhibitors. It presents very interesting internal and external statistical validation parameters (SD = 0.081; R2 = 0.903; Q2 = 0.869; ; F = 247.2). This result suggests that the 3D-QSAR combinatorial model can be used to search for new 3VHE inhibitors and predict their potential activity. Based on the combinatorial pharmacophore model, a virtual screening of the Enamine database was performed. Compounds selected after virtual screening were subjected to molecular docking protocols (HTVS, SP, XP and IFD). Twenty new active compounds have been identified and their absorption, distribution, metabolism and excretion (ADME) property calculated using Schr?dinger’s Qikprop module. These results suggest that these new compounds could constitute new chemical starting points for further structural optimization of 3VHE inhibitors.