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A Review on Parallel Virtual Screening Softwares for High-Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target asso...

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Autores principales: Murugan, Natarajan Arul, Podobas, Artur, Gadioli, Davide, Vitali, Emanuele, Palermo, Gianluca, Markidis, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780228/
https://www.ncbi.nlm.nih.gov/pubmed/35056120
http://dx.doi.org/10.3390/ph15010063
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author Murugan, Natarajan Arul
Podobas, Artur
Gadioli, Davide
Vitali, Emanuele
Palermo, Gianluca
Markidis, Stefano
author_facet Murugan, Natarajan Arul
Podobas, Artur
Gadioli, Davide
Vitali, Emanuele
Palermo, Gianluca
Markidis, Stefano
author_sort Murugan, Natarajan Arul
collection PubMed
description Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 10 [Formula: see text] to 10 [Formula: see text]), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.
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spelling pubmed-87802282022-01-22 A Review on Parallel Virtual Screening Softwares for High-Performance Computers Murugan, Natarajan Arul Podobas, Artur Gadioli, Davide Vitali, Emanuele Palermo, Gianluca Markidis, Stefano Pharmaceuticals (Basel) Article Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 10 [Formula: see text] to 10 [Formula: see text]), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures. MDPI 2022-01-04 /pmc/articles/PMC8780228/ /pubmed/35056120 http://dx.doi.org/10.3390/ph15010063 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 Article
Murugan, Natarajan Arul
Podobas, Artur
Gadioli, Davide
Vitali, Emanuele
Palermo, Gianluca
Markidis, Stefano
A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title_full A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title_fullStr A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title_full_unstemmed A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title_short A Review on Parallel Virtual Screening Softwares for High-Performance Computers
title_sort review on parallel virtual screening softwares for high-performance computers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8780228/
https://www.ncbi.nlm.nih.gov/pubmed/35056120
http://dx.doi.org/10.3390/ph15010063
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