{"title":"The next revolution in computational simulations: Harnessing AI and quantum computing in molecular dynamics","authors":"Anna Lappala","doi":"10.1016/j.sbi.2024.102919","DOIUrl":null,"url":null,"abstract":"<div><p>The integration of artificial intelligence, machine learning and quantum computing into molecular dynamics simulations is catalyzing a revolution in computational biology, improving the accuracy and efficiency of simulations. This review describes the advancements and applications of these technologies to process vast molecular dynamics simulation datasets, adapt parameters of simulations and gain insight into complex biological processes. These advances include the use of predictive force fields, adaptive algorithms and quantum-assisted methodologies. While the integration of artificial intelligence and quantum computing with MD simulations provides insightful and stimulating improvements to our understanding of molecular mechanisms, it could introduce new issues related to data quality, interpretability of models and computational complexity. Modern multidisciplinary approaches are needed to navigate these challenges and exploit the potential of these emerging technologies for MD simulations of biomolecular systems.</p></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"89 ","pages":"Article 102919"},"PeriodicalIF":6.1000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in structural biology","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959440X24001465","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of artificial intelligence, machine learning and quantum computing into molecular dynamics simulations is catalyzing a revolution in computational biology, improving the accuracy and efficiency of simulations. This review describes the advancements and applications of these technologies to process vast molecular dynamics simulation datasets, adapt parameters of simulations and gain insight into complex biological processes. These advances include the use of predictive force fields, adaptive algorithms and quantum-assisted methodologies. While the integration of artificial intelligence and quantum computing with MD simulations provides insightful and stimulating improvements to our understanding of molecular mechanisms, it could introduce new issues related to data quality, interpretability of models and computational complexity. Modern multidisciplinary approaches are needed to navigate these challenges and exploit the potential of these emerging technologies for MD simulations of biomolecular systems.
期刊介绍:
Current Opinion in Structural Biology (COSB) aims to stimulate scientifically grounded, interdisciplinary, multi-scale debate and exchange of ideas. It contains polished, concise and timely reviews and opinions, with particular emphasis on those articles published in the past two years. In addition to describing recent trends, the authors are encouraged to give their subjective opinion of the topics discussed.
In COSB, we help the reader by providing in a systematic manner:
1. The views of experts on current advances in their field in a clear and readable form.
2. Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications.
[...]
The subject of Structural Biology is divided into twelve themed sections, each of which is reviewed once a year. Each issue contains two sections, and the amount of space devoted to each section is related to its importance.
-Folding and Binding-
Nucleic acids and their protein complexes-
Macromolecular Machines-
Theory and Simulation-
Sequences and Topology-
New constructs and expression of proteins-
Membranes-
Engineering and Design-
Carbohydrate-protein interactions and glycosylation-
Biophysical and molecular biological methods-
Multi-protein assemblies in signalling-
Catalysis and Regulation