VAMPnets for deep learning of molecular kinetics
Author
Abstract
Suggested Citation
DOI: 10.1038/s41467-017-02388-1
Download full text from publisher
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Corneel Casert & Isaac Tamblyn & Stephen Whitelam, 2024. "Learning stochastic dynamics and predicting emergent behavior using transformers," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
- Benjamin D Lee & Anthony Gitter & Casey S Greene & Sebastian Raschka & Finlay Maguire & Alexander J Titus & Michael D Kessler & Alexandra J Lee & Marc G Chevrette & Paul Allen Stewart & Thiago Britto-, 2022. "Ten quick tips for deep learning in biology," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-20, March.
- Trayder Thomas & Benoît Roux, 2021. "Tyrosine kinases: complex molecular systems challenging computational methodologies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-13, October.
- Yuxuan Zhuang & Rebecca J. Howard & Erik Lindahl, 2024. "Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
- Shams Mehdi & Pratyush Tiwary, 2024. "Thermodynamics-inspired explanations of artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2023. "A Review of Data‐Driven Discovery for Dynamic Systems," International Statistical Review, International Statistical Institute, vol. 91(3), pages 464-492, December.
- Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
- Giacomo Janson & Gilberto Valdes-Garcia & Lim Heo & Michael Feig, 2023. "Direct generation of protein conformational ensembles via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-017-02388-1. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.