Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome-Reference-Cited by-同舟云学术

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Molecular Dynamics and Machine Learning Give Insights on the Flexibility–Activity Relationships in Tyrosine Kinome

Published:2023-07-18 Issue:15 Volume:63 Page:4814-4826
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Majumdar Sarmistha¹,Di Palma Francesco¹^ORCID,Spyrakis Francesca²^ORCID,Decherchi Sergio³^ORCID,Cavalli Andrea¹⁴^ORCID

Affiliation:

1. Computational & Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy

2. Department of Drug Science and Technology, University of Turin, via Giuria 9, I-10125 Turin, Italy

3. Data Science and Computation, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, I-16163 Genova, Italy

4. Department of Pharmacy and Biotechnology, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy

Funder

Partnership for Advanced Computing in Europe AISBL

UniversitÃ degli Studi di Torino, Ricerca Locale 2021

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c00738

Reference69 articles.

1. Tyrosine kinase signaling and the emergence of multicellularity

2. Evolution of Functional Diversity in the Holozoan Tyrosine Kinome

3. Receptor tyrosine kinases: from biology to pathology

4. Receptor tyrosine kinase structure and function in health and disease

5. Mechanisms of receptor tyrosine kinase activation in cancer

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