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Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

Conventional in silico modeling is often viewed as ‘one-target’ or ‘single-task’ computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that...

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Autores principales: Halder, Amit Kumar, Moura, Ana S., Cordeiro, Maria Natália D. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099502/
https://www.ncbi.nlm.nih.gov/pubmed/35563327
http://dx.doi.org/10.3390/ijms23094937
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author Halder, Amit Kumar
Moura, Ana S.
Cordeiro, Maria Natália D. S.
author_facet Halder, Amit Kumar
Moura, Ana S.
Cordeiro, Maria Natália D. S.
author_sort Halder, Amit Kumar
collection PubMed
description Conventional in silico modeling is often viewed as ‘one-target’ or ‘single-task’ computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box–Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.
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spelling pubmed-90995022022-05-14 Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next? Halder, Amit Kumar Moura, Ana S. Cordeiro, Maria Natália D. S. Int J Mol Sci Review Conventional in silico modeling is often viewed as ‘one-target’ or ‘single-task’ computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box–Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool. MDPI 2022-04-29 /pmc/articles/PMC9099502/ /pubmed/35563327 http://dx.doi.org/10.3390/ijms23094937 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Halder, Amit Kumar
Moura, Ana S.
Cordeiro, Maria Natália D. S.
Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title_full Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title_fullStr Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title_full_unstemmed Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title_short Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?
title_sort moving average-based multitasking in silico classification modeling: where do we stand and what is next?
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9099502/
https://www.ncbi.nlm.nih.gov/pubmed/35563327
http://dx.doi.org/10.3390/ijms23094937
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