Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease

William Martin, Gloria Sheynkman, Felice C. Lightstone, Ruth Nussinov, Feixiong Cheng

Research output: Contribution to journalReview articlepeer-review

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. We review select AD targets, describe current simulation methods, and introduce learning concepts and their application in AD, highlighting limitations and potential solutions.

Original languageEnglish (US)
Pages (from-to)103-113
Number of pages11
JournalCurrent Opinion in Structural Biology
Volume72
DOIs
StatePublished - Feb 2022
Externally publishedYes

ASJC Scopus subject areas

  • Structural Biology
  • Molecular Biology

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