QSAR modeling useful in anti-cancer drug discovery: Prediction of V600EBRAF-dependent p-ERK using Monte Carlo Method
Khalid Bouhedjar
Affiliation
- 1Laboratoire de Sécurité Environnementale et Alimentaire, Université Badji Mokhtar Annaba, B.P. 12, 23000 Annaba, Alegria
- 2Centre de Recherche en Biotechnologie, Ali Mendjli Nouvelle Ville UV 03, B.P. E73, 25016, Constantine, Alegria
- 3Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy
- 4Department of Electronics and Information, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy
Corresponding Author
K, Bouhedjar. Centre de Recherche en Biotechnologie, Ali Mendjli Nouvelle Ville UV 03, B.P. E73, 25016, Constantine, Alegria; Tel: +21331775044; E-mail: khalid.bouhedjar@univ-annaba.org
Citation
K, Bouhedjar., et al. QSAR Modeling useful in Anti-Cancer Drug Discovery: Prediction of V600EBRAF-Dependent P-ERK using Monte Carlo Method. (2017) J Med Chem Toxicol 2(1): 34-39.
Copy rights
© 2017 K, Bouhedjar. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.
Keywords
Abstract
Quantitative structure−activity relationship (QSAR) modeling is one of the major computer aided modeling employed in medicinal chemistry. It is used for developing relationships between the effects (e.g. activities and properties of interest) of a series of molecules with their structural properties. The aim of this work was to develop QSAR models to predict the inhibition of V600EBRAF-dependent extracellular regulated kinase (ERK) phosphorylation in WM266.4 melanoma cell lines (IC50p ERK) using the CORAL software (http://www.insilico.eu/coral). These models make use of descriptors based on simplified molecular input-line entry system (SMILES), optimized with the Monte Carlo method. The statistical quality of the newly built models was satisfactory on three random splits of data.
