ArticleJournal of computer-aided molecular design2026
Machine learning-guided drug repurposing for EGFR inhibition using scaffold-split validation, docking, and molecular dynamics.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
6 authors.
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Abstract
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 DrugBank compounds for mutant EGFR evaluation. EGFR bioactivity data from ChEMBL were curated, standardized, converted to pIC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.