Rapid screening of potent and mechanistically insightful repurposable anticancer drugs targeting EGFR for non-small cell lung cancer: machine learning-aided and structure-guided approach.

Md Shakil Ahamed, Sheikh Abdullah Al Ashik

Abstract read

In one paragraph

Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Md Shakil AhamedDepartment of Biotechnology, Bangladesh Agricultural University, Mymensingh, 2202, Bangladesh.ORCID http://orcid.org/0009-0008-9819-8591

Sheikh Abdullah Al AshikDepartment of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh. ashik.bge10@gmail.com.ORCID http://orcid.org/0000-0002-3923-1266

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the initial success of EGFR-targeted therapies in non-small cell lung cancer (NSCLC), the emergence of drug resistance remains a significant clinical challenge. While several approved anticancer drugs exist, the development of resistance to current EGFR inhibitors necessitates the identification of novel repurposable drugs and rapid strategies to screen drugs that could address resistance. Therefore, this study aimed to develop a machine learning-aided and structure-guided rapid screening framework to identify repurposable inhibitors from an anticancer drug library with the potential activity against EGFR in NSCLC. We developed a Random Forest model (cross-validated R

Indexed as

Antineoplastic AgentsCarcinoma, Non-Small-Cell LungDrug RepositioningLung NeoplasmsMachine LearningProtein Kinase InhibitorsCell Line, TumorDrug Screening Assays, AntitumorErbB ReceptorsHumansMolecular Docking SimulationMolecular Dynamics SimulationAntineoplastic AgentsEGFR protein, humanErbB ReceptorsProtein Kinase InhibitorsDrug repurposingEGFRMachine learningMolecular dockingMolecular dynamics simulationNon-small cell lung cancer

Identifiers

PMID41920244

DOI10.1016/j.ijbiomac.2017.05.035

What Socratic holds

Textmetadata

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Registered trials

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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 Socratic graph.