Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach-Reference-Cited by-同舟云学术

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

Published:2022 Issue:26 Volume:12 Page:16779-16789
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Agarwal Subhash M.¹^ORCID,Nandekar Prajwal²,Saini Ravi³

Affiliation:

1. Bioinformatics Division, ICMR-National Institute of Cancer Prevention and Research, I-7, Sector-39, Noida-201301, India

2. Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Schloss-Wolfsbrunnenweg 35, 69118, Heidelberg, Germany

3. School of Biochemical Engineering, Indian Institute of Technology (BHU), Uttar Pradesh, Varanasi 221 005, India

Abstract

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

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/RA/D2RA00373B

Reference33 articles.

1. NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database

2. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019

3. A New Golden Age of Natural Products Drug Discovery

4. Drug Discovery and Natural Products: End of an Era or an Endless Frontier?

5. Targeting EGFR in Lung Cancer: Current Standards and Developments

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