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Updated: Oct 9, 2025
Validated Immunochemical Assay for Comprehensive Determination of the Human Epidermal Growth Factor Receptor 2 Released from and Bound to Cells
Published on: May 9, 2025
Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches
Donghui Huo1, Shiyu Wang1, Yue Kong1
1State Key Laboratory of Chemical Resource Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Abstract:
The epidermal growth factor receptor (EGFR) signaling pathway plays an important role in cell growth, proliferation, differentiation, and other physiological processes, which makes the EGFR a promising target for anticancer therapies. The discovery of novel EGFR inhibitors may provide a solution to the problem of drug resistance. In this work, we performed a ligand-based virtual screening (LBVS) protocol for finding novel EGFR inhibitors from a 5.3 million compound library. First, the 3D shape-based similarity was used to obtain structurally novel EGFR inhibitors. In this study, we tried three queries; two were crystal structures and one was generated from deep generative models of graphs (DGMG). Next, we have built four structure-activity relationship (SAR) models and three quantitative structure-activity relationship (QSAR) models based on an SVM method for further screening of highly active EGFR inhibitors. Experimental validations led to the identification of nine hits out of 18 tested compounds. Among them, hit 1, hit 5, and hit 6 had IC50 values around 80 nM against EGFR whose interactions with EGFR were further investigated by molecular dynamics simulations.
Insights
Researchers identified novel epidermal growth factor receptor (EGFR) inhibitors using ligand-based virtual screening and machine learning. Nine compounds showed inhibitory activity, with three potent hits demonstrating nanomolar IC50 values against EGFR.
Area of Science:
- Oncology
- Pharmacology
- Computational Chemistry
Background:
- The epidermal growth factor receptor (EGFR) signaling pathway is crucial for cell functions and a key target in cancer therapy.
- Drug resistance necessitates the development of novel EGFR inhibitors.
Purpose of the Study:
- To discover novel EGFR inhibitors using a ligand-based virtual screening (LBVS) approach.
- To identify potent compounds that can overcome existing drug resistance mechanisms.
Main Methods:
- Conducted LBVS on a library of 5.3 million compounds using 3D shape-based similarity searches.
- Employed deep generative models of graphs (DGMG) and crystal structures for query generation.
- Developed and utilized Support Vector Machine (SVM) based structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) models.
- Performed experimental validation and molecular dynamics simulations for promising hits.
Main Results:
- Identified nine active EGFR inhibitors from 18 tested compounds.
- Three hits (hit 1, hit 5, hit 6) exhibited IC50 values around 80 nM against EGFR.
- Molecular dynamics simulations provided insights into the interactions of the top hits with EGFR.
Conclusions:
- The LBVS protocol combined with SAR/QSAR modeling is effective for identifying novel EGFR inhibitors.
- The identified potent inhibitors represent promising candidates for further development in anticancer therapies.
- Understanding inhibitor-EGFR interactions through simulations aids in drug design.
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