Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Halder, AK (Author) Other; Giri, AK (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Molecules

Vol. 24

Final page: 3909

ISSN: 1420-3049

Publisher: MDPI

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00R-8BW

DOI: 10.3390/molecules24213909

Abstract (EN): Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure-activity relationship (mt-QSAR) models based on the Box-Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 26