In Silico Exploration of Novel EGFR Kinase Mutant-Selective Inhibitors Using a Hybrid Computational Approach.

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.