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Updated: Jun 6, 2026
A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016
In Silico Exploration of Novel EGFR Kinase Mutant-Selective Inhibitors Using a Hybrid Computational Approach
Md Ali Asif Noor1, Md Mazedul Haq2, Md Arifur Rahman Chowdhury2
1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Abstract:
Targeting epidermal growth factor receptor (EGFR) mutants is a promising strategy for treating non-small cell lung cancer (NSCLC). This study focused on the computational identification and characterization of potential EGFR mutant-selective inhibitors using pharmacophore design and validation by deep learning, virtual screening, ADMET (Absorption, distribution, metabolism, excretion and toxicity), and molecular docking-dynamics simulations. A pharmacophore model was generated using Pharmit based on the potent inhibitor JBJ-125, which targets the mutant EGFR (PDB 5D41) and is used for the virtual screening of the Zinc database. In total, 16 hits were retrieved from 13,127,550 molecules and 122,276,899 conformers. The pharmacophore model was validated via DeepCoy, generating 100 inactive decoy structures for each active molecule and ADMET tests were conducted using SWISS ADME and PROTOX 3.0. Filtered compounds underwent molecular docking studies using Glide, revealing promising interactions with the EGFR allosteric site along with better docking scores. Molecular dynamics (MD) simulations confirmed the stability of the docked conformations. These results bring out five novel compounds that can be evaluated as single agents or in combination with existing therapies, holding promise for treating the EGFR-mutant NSCLC.
Insights
This study computationally identified novel inhibitors targeting mutant epidermal growth factor receptor (EGFR) for non-small cell lung cancer (NSCLC) treatment. Five promising compounds were discovered for potential therapeutic evaluation against EGFR-mutant NSCLC.
Area of Science:
- Computational chemistry
- Drug discovery
- Oncology
Background:
- Targeting mutant epidermal growth factor receptor (EGFR) is a key strategy for non-small cell lung cancer (NSCLC).
- Development of selective inhibitors is crucial for effective and safe treatment of EGFR-mutant NSCLC.
Purpose of the Study:
- To computationally identify and characterize novel EGFR mutant-selective inhibitors.
- To explore potential therapeutic agents for EGFR-mutant non-small cell lung cancer.
Main Methods:
- Pharmacophore modeling and virtual screening of the Zinc database.
- Deep learning-based validation, ADMET prediction, and molecular docking-dynamics simulations.
- Utilized Pharmit, DeepCoy, SWISS ADME, PROTOX 3.0, and Glide for computational analysis.
Main Results:
- Identified 16 potential hits from over 13 million molecules.
- Five novel compounds showed promising interactions with the EGFR allosteric site.
- Molecular dynamics simulations confirmed the stability of docked conformations.
Conclusions:
- The identified novel compounds hold promise for treating EGFR-mutant NSCLC.
- These compounds can be evaluated as single agents or in combination therapies.
- This computational approach facilitates the discovery of targeted cancer therapies.
