• Home
  • Search
  • DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors
  • https://doi.org/10.1038/s41598-025-22126-8Copy DOI Icon

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors

Show More
  • Abstract
  • Literature Map
  • References
  • Similar Papers
Abstract

Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learning technologies, it is now possible to simulate interactions with high precision between EGFR and small molecules to predict inhibitory/ modulatory activity at an unprecedented scale. In this work, we propose a novel machine-learning method to fast and precise classification of small compounds that are active, intermediate or inactive in inhibiting/modulating EGFR activity. We developed DeepEGFR, a novel multi-class graph neural network (GNN) model, to classify compounds into Active, Inactive, and Intermediate functional categories. DeepEGFR leverages complementary molecular representations, combining SMILES strings and molecular fingerprint matrices (Klekota-Roth and PubChem) to capture both structural and property-based features of compounds. The model constructs an advanced molecular graph representing atom type, formal charge, bond type, and bond order, through nodes and edges. DeepEGFR achieved superior performance compared to baseline machine learning algorithms (e.g., SVM, Random Forest, ANN), with approximately 94% F1-scores across training and test datasets for all activity classes. To ensure interpretability, the top 20 features identified by DeepEGFR were validated against the five key characteristics of FDA-approved EGFR inhibitors (Afatinib, Gefitinib, Osimertinib, Dacomitinib, Erlotinib), confirming the biological relevance of the features. Moreover, DeepEGFR successfully identified 300 underexplored EGFR-targeting compounds, demonstrating its potential to accelerate the discovery of therapeutic agents. These results highlight the effectiveness of graph neural networks in advancing molecular activity classification, setting a potential new benchmark for EGFR inhibitor prediction. These findings demonstrate the DeepEGFR’s ability to highlight the promising EGFR inhibitors, that have received limited prior investigation, thereby supporting its role in facilitating the rational development of targeted therapies for precision oncology.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22126-8.

Similar Papers
  • PDF
  • Research Article
  • Citations156

The Role of Individual SH2 Domains in Mediating Association of Phospholipase C-γ1 with the Activated EGF Receptor

  • Sep 01, 1999
  • Journal of Biological Chemistry
  • Ansuman Chattopadhyay +4
  • Research Article
  • Citations25

FGFR4 increases EGFR oncogenic signaling in lung adenocarcinoma, and their combined inhibition is highly effective

  • Feb 08, 2019
  • Lung Cancer
  • Alvaro Quintanal-Villalonga +10
  • Research Article

Abstract 619: Dual targeted therapies to overcome tyrosin kinase inhibitors resistant in epidermal growth factor receptor (EGFR) expression of lung cancer

  • Apr 15, 2010
  • Cancer Research
  • Wen-Chien Huang
  • Research Article
  • Citations65

Porcine Epidemic Diarrhea Virus-Induced Epidermal Growth Factor Receptor Activation Impairs the Antiviral Activity of Type I Interferon.

  • Mar 28, 2018
  • Journal of Virology
  • Lijun Yang +7
  • Research Article
  • Citations3

Epidermal growth factor receptor (EGFR) inhibition in triple-negative breast cancer (BrCa)

  • Jun 20, 2007
  • Journal of Clinical Oncology
  • B Corkery +3
  • Research Article
  • Citations23

The importance of optimal drug sequencing in metastatic colorectal cancer: biological rationales for the observed survival benefit conferred by first-line treatment with EGFR inhibitors

  • Jun 12, 2015
  • Expert Opinion on Biological Therapy
  • Zev A Wainberg +1
  • Research Article
  • Citations35

MEK and EGFR inhibition demonstrate synergistic activity in EGFR-dependent NSCLC

  • Mar 15, 2009
  • Cancer Biology & Therapy
  • Justin M Balko +3
  • Preprint Article

Data from Role of Cell Cycle in Epidermal Growth Factor Receptor Inhibitor-Mediated Radiosensitization

  • Mar 30, 2023
  • Aarif Ahsan +4
  • Preprint Article

Data from Role of Cell Cycle in Epidermal Growth Factor Receptor Inhibitor-Mediated Radiosensitization

  • Mar 30, 2023
  • Aarif Ahsan +4
  • Research Article
  • Citations4

Sleep deprivation effects on EGFR signaling in a zebrafish exposed to rotenone

  • Jan 10, 2024
  • Behavioural brain research
  • Marina Kniazkina +1
  • Research Article
  • Citations3

Inhibition of EGFR attenuates EGF-induced activation of retinal pigment epithelium cell via EGFR/AKT signaling pathway.

  • Jun 18, 2024
  • International journal of ophthalmology
  • Yu-Sheng Zhu +3
  • Research Article
  • Citations69

ERK1/2 Mediate Wounding- and G-protein-Coupled Receptor Ligands-Induced EGFR Activation via Regulating ADAM17 and HB-EGF Shedding

  • Jul 24, 2008
  • Investigative Opthalmology & Visual Science
  • Jia Yin +1
  • Research Article
  • Citations69

Mechanisms of Disease: radiosensitization by epidermal growth factor receptor inhibitors

  • Dec 01, 2004
  • Nature Clinical Practice Oncology
  • Carolyn I Sartor
  • Discussion
  • Citations4

COX-2 and EGFR: Partners in Crime Split by Aspirin

  • Apr 21, 2015
  • EBioMedicine
  • Paola Patrignani +1
  • Research Article
  • Citations1

Abstract 2397: Correlation between biomarker status and response to EGFR inhibition in triple-negative breast cancer (TNBC): findings from a Phase II trial.

  • Apr 15, 2013
  • Cancer Research
  • Denise Yardley +9
Cactus Communications logo

Copyright 2026 Cactus Communications. All rights reserved.