Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease
Abstract
The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. Finally, we review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.
- Authors:
-
[1];
[5]
- Cleveland Clinic, OH (United States)
- Univ. of Virginia, Charlottesville, VA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- National Cancer Institute, Frederick, MD (United States); Tel Aviv Univ., Tel Aviv (Israel)
- Cleveland Clinic, OH (United States); Case Western Reserve Univ., Cleveland, OH (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA); National Institute of Aging; National Heart, Lung, and Blood Institute; National Institutes of Health (NIH)
- OSTI Identifier:
- 1832329
- Report Number(s):
- LLNL-JRNL-827311
Journal ID: ISSN 0959-440X; 1042416
- Grant/Contract Number:
- AC52-07NA27344; R01AG066707; 3R01AG066707-01S1; R00HL138272; HHSN261200800001E
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Current Opinion in Structural Biology
- Additional Journal Information:
- Journal Volume: 72; Journal Issue: na; Journal ID: ISSN 0959-440X
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Biological and medical sciences
Citation Formats
Martin, William, Sheynkman, Gloria, Lightstone, Felice C., Nussinov, Ruth, and Cheng, Feixiong. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. United States: N. p., 2021.
Web. doi:10.1016/j.sbi.2021.09.001.
Martin, William, Sheynkman, Gloria, Lightstone, Felice C., Nussinov, Ruth, & Cheng, Feixiong. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. United States. https://doi.org/10.1016/j.sbi.2021.09.001
Martin, William, Sheynkman, Gloria, Lightstone, Felice C., Nussinov, Ruth, and Cheng, Feixiong. Thu .
"Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease". United States. https://doi.org/10.1016/j.sbi.2021.09.001. https://www.osti.gov/servlets/purl/1832329.
@article{osti_1832329,
title = {Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease},
author = {Martin, William and Sheynkman, Gloria and Lightstone, Felice C. and Nussinov, Ruth and Cheng, Feixiong},
abstractNote = {The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of molecular dynamics simulations. To date, these capabilities extend from running very long simulations (tens to hundreds of microseconds) to thousands of short simulations. However, the expansive data generated in these simulations must be made interpretable not only by the investigator who performs them but also by others as well. Here, we demonstrate how integrating learning techniques, such as artificial intelligence, machine learning, and neural networks, into analysis pipelines can reveal the kinetics of Alzheimer's disease (AD) protein aggregation. Finally, we review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.},
doi = {10.1016/j.sbi.2021.09.001},
journal = {Current Opinion in Structural Biology},
number = na,
volume = 72,
place = {United States},
year = {Thu Oct 07 00:00:00 EDT 2021},
month = {Thu Oct 07 00:00:00 EDT 2021}
}
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