Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning-guided drug repurposing for EGFR inhibition using scaffold-split validation, docking, and molecular dynamics.

Journal of computer-aided molecular design - 8 Jun 2026

Akinnusi Precious A, Egunjobi Gladys D, Akinnusi Ayomide, Oluwadare Tomilola, Ogunbiyi Fayowole, Shodehinde Sidiqat A

Abstract excerpt

Aberrant epidermal growth factor receptor (EGFR) signaling drives multiple cancers, but the clinical effectiveness of EGFR inhibitors is limited by relapse, toxicity, and mutation-associated resistance. This study applied an integrated computational drug-repurposing workflow combining machine learning-based potency prediction, structure-based docking, and molecular dynamics simulation to prioritize approved...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Drug Repositioning
Share to ErbB Receptors

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to EGFR gene

Share to Machine Learning

Share to Molecular Docking Simulation

Share to Molecular Dynamics Simulation

Share to Mutation

Share to Protein Kinase Inhibitors

Source

Identifiers

PMID: 42257754
DOI: 10.1007/s10822-026-00853-y

Genes

EGFR

Variants

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning-guided drug repurposing for EGFR inhibition using scaffold-split validation, docking, and molecular dynamics.PMID 42257754Read ↗

Identification of EGFR inhibitors as potential agents for cancer therapy: pharmacophore-based modeling, molecular docking, and molecular dynamics investigations.Semantic proximity Read ↗

Systematic Benchmarking of Kinase Bioactivity Models Across Splitting Strategies and Protein RepresentationsSemantic proximity Read ↗

PROtein Analytics for Kinase Therapeutic Inhibitor Variants (PROAKTIV): A Machine learning approach to predict efficacy of tyrosine kinase inhibitors against mutations in EGFR, ALK and BRAFSemantic proximity Read ↗

AI-driven design of Novel EGFR Inhibitors with Enhanced Potency and Drug-Like PropertiesSemantic proximity Read ↗

Artificial Intelligence in Drug Discovery: A New Paradigm from Target Identification to Clinical TranslationSemantic proximity Read ↗

A systematic pipeline of protein structure selection for computer-aided drug discovery: A case study on T790M/L858R mutant EGFR structures.Semantic proximity Read ↗

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the clinical treatment of GlioblastomaSemantic proximity Read ↗

Structure-based classification of EGFR mutations informs inhibitor selection for lung cancer therapy.Semantic proximity Read ↗

Drug repurposing for SARS-CoV-2: a high-throughput molecular docking, molecular dynamics, machine learning, and DFT studySemantic proximity Read ↗

Scaffold-Aware QSAR, BRICS Analog Design, and Docking Consensus for Prioritizing KRAS G12C-Targeted Cancer Therapeutic LeadsSemantic proximity Read ↗

Toward structure-based drug design against the epidermal growth factor receptor (EGFR).Semantic proximity Read ↗

Identification of novel drug-resistant EGFR mutant inhibitors by in silico screening using comprehensive assessments of protein structures.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.