A knowledge-guided pre-training framework for improving molecular representation learning
Author
Abstract
Suggested Citation
DOI: 10.1038/s41467-023-43214-1
Download full text from publisher
References listed on IDEAS
- Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
- Alexandre Tkatchenko, 2020. "Machine learning for chemical discovery," Nature Communications, Nature, vol. 11(1), pages 1-4, December.
- Tobias Klein & Navratna Vajpai & Jonathan J. Phillips & Gareth Davies & Geoffrey A. Holdgate & Chris Phillips & Julie A. Tucker & Richard A. Norman & Andrew D. Scott & Daniel R. Higazi & David Lowe & , 2015. "Structural and dynamic insights into the energetics of activation loop rearrangement in FGFR1 kinase," Nature Communications, Nature, vol. 6(1), pages 1-12, November.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Huziel E. Sauceda & Luis E. Gálvez-González & Stefan Chmiela & Lauro Oliver Paz-Borbón & Klaus-Robert Müller & Alexandre Tkatchenko, 2022. "BIGDML—Towards accurate quantum machine learning force fields for materials," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
- Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Gabriele Orlando & Daniele Raimondi & Ramon Duran-Romaña & Yves Moreau & Joost Schymkowitz & Frederic Rousseau, 2022. "PyUUL provides an interface between biological structures and deep learning algorithms," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- Gang Wang & Shinya Mine & Duotian Chen & Yuan Jing & Kah Wei Ting & Taichi Yamaguchi & Motoshi Takao & Zen Maeno & Ichigaku Takigawa & Koichi Matsushita & Ken-ichi Shimizu & Takashi Toyao, 2023. "Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Sukriti Manna & Troy D. Loeffler & Rohit Batra & Suvo Banik & Henry Chan & Bilvin Varughese & Kiran Sasikumar & Michael Sternberg & Tom Peterka & Mathew J. Cherukara & Stephen K. Gray & Bobby G. Sumpt, 2022. "Learning in continuous action space for developing high dimensional potential energy models," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Ribeiro, Haroldo V. & Lopes, Diego D. & Pessa, Arthur A.B. & Martins, Alvaro F. & da Cunha, Bruno R. & Gonçalves, Sebastián & Lenzi, Ervin K. & Hanley, Quentin S. & Perc, Matjaž, 2023. "Deep learning criminal networks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
- Xinyu Chen & Yufeng Xie & Yaochen Sheng & Hongwei Tang & Zeming Wang & Yu Wang & Yin Wang & Fuyou Liao & Jingyi Ma & Xiaojiao Guo & Ling Tong & Hanqi Liu & Hao Liu & Tianxiang Wu & Jiaxin Cao & Sitong, 2021. "Wafer-scale functional circuits based on two dimensional semiconductors with fabrication optimized by machine learning," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
- Kangming Li & Daniel Persaud & Kamal Choudhary & Brian DeCost & Michael Greenwood & Jason Hattrick-Simpers, 2023. "Exploiting redundancy in large materials datasets for efficient machine learning with less data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
- Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
- Kai Li & Jifeng Wang & Yuanyuan Song & Ying Wang, 2023. "Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Yilei Wu & Chang-Feng Wang & Ming-Gang Ju & Qiangqiang Jia & Qionghua Zhou & Shuaihua Lu & Xinying Gao & Yi Zhang & Jinlan Wang, 2024. "Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
- Yasuhiro Yoshikai & Tadahaya Mizuno & Shumpei Nemoto & Hiroyuki Kusuhara, 2024. "Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Özhan Şimşek, 2024. "Machine Learning Offers Insights into the Impact of In Vitro Drought Stress on Strawberry Cultivars," Agriculture, MDPI, vol. 14(2), pages 1-17, February.
- Jose Antonio Garrido Torres & Vahe Gharakhanyan & Nongnuch Artrith & Tobias Hoffmann Eegholm & Alexander Urban, 2021. "Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
- Shufeng Kong & Francesco Ricci & Dan Guevarra & Jeffrey B. Neaton & Carla P. Gomes & John M. Gregoire, 2022. "Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Deeptha Ishwar & Rupa Haldavnekar & Krishnan Venkatakrishnan & Bo Tan, 2022. "Minimally invasive detection of cancer using metabolic changes in tumor-associated natural killer cells with Oncoimmune probes," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
- Yang Shi & Junyu Ren & Guanyu Chen & Wei Liu & Chuqi Jin & Xiangyu Guo & Yu Yu & Xinliang Zhang, 2022. "Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- Tian Xie & Arthur France-Lanord & Yanming Wang & Jeffrey Lopez & Michael A. Stolberg & Megan Hill & Graham Michael Leverick & Rafael Gomez-Bombarelli & Jeremiah A. Johnson & Yang Shao-Horn & Jeffrey C, 2022. "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties," Nature Communications, Nature, vol. 13(1), pages 1-10, 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:14:y:2023:i:1:d:10.1038_s41467-023-43214-1. 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.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.