TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors-Reference-Cited by-同舟云学术

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TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors

Published:2020-09 Issue:3 Volume:96 Page:921-930
ISSN:1747-0277
Container-title:Chemical Biology & Drug Design
language:en
Short-container-title:Chem Biol Drug Des

Author:

Saini Ravi¹,Fatima Shehnaz²,Agarwal Subhash Mohan²^ORCID

Affiliation:

1. School of Biochemical Engineering Indian Institute of Technology (BHU) Varanasi India

2. Bioinformatics Division ICMR‐National Institute of Cancer Prevention and Research Noida India

Publisher

Wiley

Subject

Molecular Medicine,Biochemistry,Drug Discovery,Pharmacology,Organic Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cbdd.13697

Reference32 articles.

1. Insight into discovery of next generation reversible TMLR inhibitors targeting EGFR activating and drug resistant T790M mutants;Agarwal S. M.;Current Cancer Drug Targets,2017

2. ChemmineR: a compound mining framework for R

3. Pros and cons of virtual screening based on public “Big Data”: In silico mining for new bromodomain inhibitors

4. QSAR-Based Models for Designing Quinazoline/Imidazothiazoles/Pyrazolopyrimidines Based Inhibitors against Wild and Mutant EGFR

5. NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors

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