ReviewCurrent opinion in structural biology2022
Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease.
Review in Current opinion in structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Artificial Intelligence to Guide Repurposing of Drugs.Annual review of medicine · 2026Review
- Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.GeroScience · 2025Review
- Eyecare-cloud: an innovative electronic medical record cloud platform for pediatric research and clinical care.The EPMA journal · 2024Article
- Embracing exascale computing in nucleic acid simulations.Current opinion in structural biology · 2024Review
- Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease.Cell reports. Medicine · 2024Review
- Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.Journal of chemical information and modeling · 2023Review
- Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint.The journal of physical chemistry. B · 2023Article
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.