Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach Article Swipe

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Subhash Mohan Agarwal , Prajwal P. Nandekar , Ravi Saini ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1039/d2ra00373b

Identification of ADMET adherent natural products that have high affinity for double mutated drug-resistant EGFR.

Related Topics

Molecular Dynamics

Chemistry

Natural Product

Epidermal Growth Factor Receptor

Ligand (Biochemistry)

Drug Discovery

New Mutants

Computational Biology

Concepts

T790M Docking (animal) Molecular dynamics Chemistry Natural product Epidermal growth factor receptor Ligand (biochemistry) Drug discovery Mutant EGFR inhibitors Computational biology Hydrogen bond Molecule Binding site Small molecule Target protein Stereochemistry Computational chemistry Biochemistry Receptor Biology Gene Organic chemistry Gefitinib Medicine Nursing

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Type: article
Language: en
Landing Page: https://doi.org/10.1039/d2ra00373b
PDF: https://pubs.rsc.org/en/content/articlepdf/2022/ra/d2ra00373b
OA Status: gold
Cited By: 26
References: 35
Related Works: 10
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DOI: https://doi.org/10.1039/d2ra00373b

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Title: Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-01-01

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Authors: Subhash Mohan Agarwal, Prajwal P. Nandekar, Ravi Saini

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Landing page: https://doi.org/10.1039/d2ra00373b

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Concepts: T790M, Docking (animal), Molecular dynamics, Chemistry, Natural product, Epidermal growth factor receptor, Ligand (biochemistry), Drug discovery, Mutant, EGFR inhibitors, Computational biology, Hydrogen bond, Molecule, Binding site, Small molecule, Target protein, Stereochemistry, Computational chemistry, Biochemistry, Receptor, Biology, Gene, Organic chemistry, Gefitinib, Medicine, Nursing

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Cited by: 26

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Citations by year (recent): 2025: 8, 2024: 8, 2023: 10

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Related works (count): 10

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