Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics-Reference-Cited by-同舟云学术

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Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Published:2021-04-12 Issue:15 Volume:12 Page:3724-3732
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Marchetti Filippo¹²,Moroni Elisabetta³,Pandini Alessandro⁴^ORCID,Colombo Giorgio¹³^ORCID

Affiliation:

1. Department of Chemistry, Università Degli Studi di Pavia, Viale Taramelli 12, 27100 Pavia, Italy

2. Università Degli Studi di Milano, Via C. Golgi, 19, I-20133 Milan, Italy

3. Istituto di Scienze e Tecnologie Chimiche, Via Mario Bianco 9, 20131 Milano, Italy

4. Brunel University London, Uxbridge UB8 3PH, U.K.

Funder

Associazione Italiana per la Ricerca sul Cancro

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.1c00045

Reference61 articles.

1. Rescuing drug discovery: in vivo systems pathology and systems pharmacology

2. Transforming cancer drug discovery with Big Data and AI

3. How many drug targets are there?

4. Network pharmacology: the next paradigm in drug discovery

5. Multimodal small-molecule screening for human prion protein binders

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1. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects;Machine Learning with Applications;2024-09

2. Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery;ACS Medicinal Chemistry Letters;2024-08-30

3. Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins;Expert Opinion on Drug Discovery;2024-08-06

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