Literature DB >> 34560276

Machine-learning methods for ligand-protein molecular docking.

Kevin Crampon1, Alexis Giorkallos2, Myrtille Deldossi2, Stéphanie Baud3, Luiz Angelo Steffenel4.   

Abstract

Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learning (ML), especially deep learning (DL), a subset of ML, is transforming the field by tackling the associated challenges.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Data representation; Deep learning; Machine learning; Molecular docking; Sampling; Scoring

Mesh:

Substances:

Year:  2021        PMID: 34560276     DOI: 10.1016/j.drudis.2021.09.007

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  4 in total

Review 1.  Protein Function Analysis through Machine Learning.

Authors:  Chris Avery; John Patterson; Tyler Grear; Theodore Frater; Donald J Jacobs
Journal:  Biomolecules       Date:  2022-09-06

Review 2.  Methods for the Discovery and Identification of Small Molecules Targeting Oxidative Stress-Related Protein-Protein Interactions: An Update.

Authors:  Xuexuan Wu; Qiuyue Zhang; Yuqi Guo; Hengheng Zhang; Xiaoke Guo; Qidong You; Lei Wang
Journal:  Antioxidants (Basel)       Date:  2022-03-23

3.  Investigation of the In Vivo, In Vitro, and In Silico Wound Healing Potential of Pinctada martensii Purified Peptides.

Authors:  Ting Zhang; Faming Yang; Xiaoming Qin; Xianmei Yang; Chaohua Zhang; Zhaoyi Wan; Haisheng Lin
Journal:  Mar Drugs       Date:  2022-06-26       Impact factor: 6.085

Review 4.  Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development.

Authors:  Xiao-Huan Liu; Ting Cheng; Bao-Yu Liu; Jia Chi; Ting Shu; Tao Wang
Journal:  Front Pharmacol       Date:  2022-08-09       Impact factor: 5.988

  4 in total

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