A geometric deep learning approach to predict binding conformations of bioactive molecules-Reference-Cited by-同舟云学术

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A geometric deep learning approach to predict binding conformations of bioactive molecules

Published:2021-12 Issue:12 Volume:3 Page:1033-1039
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Méndez-Lucio Oscar^ORCID,Ahmad Mazen,del Rio-Chanona Ehecatl Antonio,Wegner Jörg Kurt^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-021-00409-9.pdf

Reference39 articles.

1. Hert, J., Irwin, J. J., Laggner, C., Keiser, M. J. & Shoichet, B. K. Quantifying biogenic bias in screening libraries. Nat. Chem. Biol. 5, 479–483 (2009).

2. Dobson, C. M. Chemical space and biology. Nature 432, 824–828 (2004).

3. Congreve, M., Murray, C. W. & Blundell, T. L. Keynote review: structural biology and drug discovery. Drug Discov. Today 10, 895–907 (2005).

4. Klebe, G. in Drug Design: Methodology, Concepts and Mode-of-Action (ed. Klebe, G.) 61–88 (Springer, 2013).

5. Renaud, J. P. et al. Cryo-EM in drug discovery: achievements, limitations and prospects. Nat. Rev. Drug Discov. 17, 471–492 (2018).

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