Editorial: Machine Learning in Biomolecular Simulations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22330%2F19%3A43918099" target="_blank" >RIV/60461373:22330/19:43918099 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.frontiersin.org/articles/10.3389/fmolb.2019.00076/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fmolb.2019.00076/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fmolb.2019.00076" target="_blank" >10.3389/fmolb.2019.00076</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Editorial: Machine Learning in Biomolecular Simulations

Popis výsledku v původním jazyce

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. First, machine learning needs data to learn on. Huge data sets from Internet, Internet of Things, social networks, phones, wearable devices, and other sources are now available. Such datasets were not available a decade ago. Second, the recent wave of machine learning benefits from hardware advances, in particular from computing on graphical processing units and specialized hardware. Biomolecular modeling and simulations are an ideal field for the application of machine learning approaches in the spirit of the recent boom of machine learning. Biomolecular simulations produce large amounts of data in the form of trajectories that can be used to train machine learning algorithms. At the same time, vast amounts of genomic data were critical in allowing AlphaFold in leading the field of de novo protein prediction in the most recent CASP protein prediction round. Moreover, GPUs are routinely used in biomolecular simulations for more than a decade to offload critical parts of calculation. This Research Topic collects eight innovative works showcasing the application of machine learning in biomolecular simulations and related fields. It demonstrates major machine learning approaches such as artificial neural networks, random forests, and non-linear dimensionality reduction methods. These techniques are applied in analysis of trajectories, acceleration of biomolecular simulations, parametrization of force fields, and other tasks.

Název v anglickém jazyce

Editorial: Machine Learning in Biomolecular Simulations

Popis výsledku anglicky

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. First, machine learning needs data to learn on. Huge data sets from Internet, Internet of Things, social networks, phones, wearable devices, and other sources are now available. Such datasets were not available a decade ago. Second, the recent wave of machine learning benefits from hardware advances, in particular from computing on graphical processing units and specialized hardware. Biomolecular modeling and simulations are an ideal field for the application of machine learning approaches in the spirit of the recent boom of machine learning. Biomolecular simulations produce large amounts of data in the form of trajectories that can be used to train machine learning algorithms. At the same time, vast amounts of genomic data were critical in allowing AlphaFold in leading the field of de novo protein prediction in the most recent CASP protein prediction round. Moreover, GPUs are routinely used in biomolecular simulations for more than a decade to offload critical parts of calculation. This Research Topic collects eight innovative works showcasing the application of machine learning in biomolecular simulations and related fields. It demonstrates major machine learning approaches such as artificial neural networks, random forests, and non-linear dimensionality reduction methods. These techniques are applied in analysis of trajectories, acceleration of biomolecular simulations, parametrization of force fields, and other tasks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/LTC18074" target="_blank" >LTC18074: Autokodéry pro víceúrovňové simulace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Molecular Biosciences

ISSN

2296-889X

e-ISSN

—

Svazek periodika

6

Číslo periodika v rámci svazku

AUG

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

2

Strana od-do

"76-i"-"76-ii"

Kód UT WoS článku

000483417500001

EID výsledku v databázi Scopus

2-s2.0-85072070167