Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein–ligand interactions in SARS-CoV-2 M<sup>pro</sup>-Reference-Cited by-同舟云学术

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Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein–ligand interactions in SARS-CoV-2 M^pro

Published:2023 Issue:48 Volume:13 Page:34249-34261
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Mustali Jessica¹^ORCID,Yasuda Ikki²,Hirano Yoshinori²^ORCID,Yasuoka Kenji²,Gautieri Alfonso¹,Arai Noriyoshi²^ORCID

Affiliation:

1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy

2. Department of Mechanical Engineering, Keio University, Japan

Abstract

Using SARS-CoV-2 Mpro as a case study, Wasserstein distance and dimension reduction are applied to the analysis of MD data of flexible complexes. The resulting embedding map correlates ligand-induced conformational differences and binding affinity.

Funder

Japan Society for the Promotion of Science

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RA/D3RA06375E

Reference114 articles.

1. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

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5. Computational Methods in Drug Discovery

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