Editorial: Machine Learning in Biomolecular Simulations
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"Interest in machine learning is growing in all fields of science, industry, and business. This interest was not primarily initiated by new theoretical findings. Interestingly, the theoretical basis of the majority of machine learning techniques, such as artificial neural networks, decision trees, or kernel methods, have been known for a relatively long time. Instead, there are other effects that triggered the recent boom of machine learning."
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The innovation in development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules.
“The innovation in the development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [189]
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AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
Burn, M. J., & Popelier, P. L. A. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.27006 · Cited by 15 · see citing works
Zolotoukhina, T., Yamada, M., & Iwakura, S. (2021) · Biosensors · doi:10.3390/bios11020037 · Cited by 7 · see citing works
Artificial Intelligence-Assisted Production of Biomolecules
Wani, A. W., Khalangre, A. B., Rajan, R., Mirza, A. A., Yaseen, I., Abdi, G., Rahim, A., Tarighat, M. A., & Ingle, K. P. (2023) · Springer eBooks · doi:10.1007/978-981-19-7911-8_6 · Cited by 1 · see citing works
- Authors
- Verkhivker, G., Spiwok, V., & Gervasio, F. L.
- Affiliations
- Chapman University (US)
- University College London (GB)
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2019-08-29
- Volume
- 6
- Pages
- 76-76
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC6727355
- OpenAlex
- W2968756798
- Cited by
- 4
- References
- 0
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 1
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Chapman University Digital Commons (Chapman University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- machine learning, biomolecular simulation, artificial neural networks, decision tree, kernel methods, molecular dynamics
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