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Updated: Jan 4, 2026
Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
Published on: May 29, 2021
Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential
Amit Kumar Halder1, Amal Kanta Giri2, Maria Natália Dias Soeiro Cordeiro3
1Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal. amit.halder@fc.up.pt.
Abstract:
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.
Insights
This study developed computational models to identify new cancer-fighting drugs targeting extracellular regulated kinase (ERK) enzymes. These in-silico methods successfully screened potential inhibitors, offering guidelines for novel cancer therapy development.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Cancer Biology
Background:
- Extracellular regulated kinase (ERK)-1 and ERK-2 are crucial in cellular processes, and their dysregulation is linked to cancer development.
- ERK-1/2 inhibitors represent a promising therapeutic strategy for cancer treatment.
Purpose of the Study:
- To develop robust multitarget quantitative structure-activity relationship (mt-QSAR) models for identifying novel ERK inhibitors.
- To utilize in-silico techniques for screening and validating potential drug candidates against ERK enzymes.
Main Methods:
- Development of linear and non-linear mt-QSAR models using linear discriminant analysis (LDA) and random forest (RF) techniques.
- Fragment analysis to determine structural contributions to ERK inhibition.
- Virtual screening of a chemical library, followed by molecular docking and dynamics simulations.
Main Results:
- A linear mt-QSAR model provided insights into structural requirements for ERK inhibition.
- Non-linear RF models achieved high predictive accuracy and were used to identify potential virtual hits.
- Fragment analysis validated the selection of virtual hits, and docking/dynamics simulations elucidated their interactions with ERK enzymes.
Conclusions:
- In-silico approaches, including mt-QSAR, fragment analysis, virtual screening, and molecular dynamics, are effective for discovering novel ERK inhibitors.
- The findings offer valuable guidance for the rational design of new anti-cancer agents targeting the ERK pathway.
