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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/15/1/63 |
| _version_ | 1797491279053979648 |
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| author | Natarajan Arul Murugan Artur Podobas Davide Gadioli Emanuele Vitali Gianluca Palermo Stefano Markidis |
| author_facet | Natarajan Arul Murugan Artur Podobas Davide Gadioli Emanuele Vitali Gianluca Palermo Stefano Markidis |
| author_sort | Natarajan Arul Murugan |
| collection | DOAJ |
| 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula> to 10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>12</mn></msup></semantics></math></inline-formula>), 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. |
| first_indexed | 2024-03-10T00:45:10Z |
| format | Article |
| id | doaj.art-7a921a0fa8b44990b1fb8ec666752ebf |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-10T00:45:10Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-7a921a0fa8b44990b1fb8ec666752ebf2023-11-23T15:01:30ZengMDPI AGPharmaceuticals1424-82472022-01-011516310.3390/ph15010063A Review on Parallel Virtual Screening Softwares for High-Performance ComputersNatarajan Arul Murugan0Artur Podobas1Davide Gadioli2Emanuele Vitali3Gianluca Palermo4Stefano Markidis5Department of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, SwedenDepartment of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, SwedenDipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, ItalyDipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, ItalyDipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano, 20133 Milano, ItalyDepartment of Computer Science, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, SE-10044 Stockholm, SwedenDrug 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula> to 10<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>12</mn></msup></semantics></math></inline-formula>), 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.https://www.mdpi.com/1424-8247/15/1/63computational drug discoveryvirtual screeningmolecular dockingchemical spaceparallelizationhigh-performance computers and accelerators |
| spellingShingle | Natarajan Arul Murugan Artur Podobas Davide Gadioli Emanuele Vitali Gianluca Palermo Stefano Markidis A Review on Parallel Virtual Screening Softwares for High-Performance Computers Pharmaceuticals computational drug discovery virtual screening molecular docking chemical space parallelization high-performance computers and accelerators |
| 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 | computational drug discovery virtual screening molecular docking chemical space parallelization high-performance computers and accelerators |
| url | https://www.mdpi.com/1424-8247/15/1/63 |
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