Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR-Reference-Cited by-同舟云学术

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Machine learning facilitated structural activity relationship approach for the discovery of novel inhibitors targeting EGFR

Published:2023-02-10 Issue: Volume: Page:1-19
ISSN:0739-1102
Container-title:Journal of Biomolecular Structure and Dynamics
language:en
Short-container-title:Journal of Biomolecular Structure and Dynamics

Author:

Choudhary Rekha¹,Walhekar Vinayak¹,Muthal Amol²,Kumar Dilip¹³⁴,Bagul Chandrakant¹,Kulkarni Ravindra¹^ORCID

Affiliation:

1. Department of Pharmaceutical Chemistry, BVDU’S Poona College of Pharmacy, Pune, Maharashtra, India

2. Department of Pharmacology, BVDU’S Poona College of Pharmacy, Pune, Maharashtra, India

3. Department of Entomology, University of California, Davis, Davis, California, USA

4. UC Davis Comprehensive Cancer Centre, University of California, Davis, Davis, California, USA

Publisher

Informa UK Limited

Subject

Molecular Biology,General Medicine,Structural Biology

Link

https://www.tandfonline.com/doi/pdf/10.1080/07391102.2023.2175263

Reference72 articles.

1. Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets

2. Globally Approved EGFR Inhibitors: Insights into Their Syntheses, Target Kinases, Biological Activities, Receptor Interactions, and Metabolism

3. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

4. Structural insights into conformational stability of both wild-type and mutant EZH2 receptor

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