Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods
Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Drugs and Drug Candidates |
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| Online Access: | https://www.mdpi.com/2813-2998/2/2/17 |
| _version_ | 1797586952147435520 |
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| author | Tiago Alves de Oliveira Michel Pires da Silva Eduardo Habib Bechelane Maia Alisson Marques da Silva Alex Gutterres Taranto |
| author_facet | Tiago Alves de Oliveira Michel Pires da Silva Eduardo Habib Bechelane Maia Alisson Marques da Silva Alex Gutterres Taranto |
| author_sort | Tiago Alves de Oliveira |
| collection | DOAJ |
| description | Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process. |
| first_indexed | 2024-03-11T00:29:28Z |
| format | Article |
| id | doaj.art-7f2014f9c8064aa5874d18ddb619ac6c |
| institution | Directory Open Access Journal |
| issn | 2813-2998 |
| language | English |
| last_indexed | 2024-03-11T00:29:28Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drugs and Drug Candidates |
| spelling | doaj.art-7f2014f9c8064aa5874d18ddb619ac6c2023-11-18T22:45:46ZengMDPI AGDrugs and Drug Candidates2813-29982023-05-012231133410.3390/ddc2020017Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning MethodsTiago Alves de Oliveira0Michel Pires da Silva1Eduardo Habib Bechelane Maia2Alisson Marques da Silva3Alex Gutterres Taranto4Department of Bioengineering, Federal University of São João del-Rei, Praça Dom Helvécio, 74-Fábricas, São João del-Rei 36301-1601, BrazilFederal Center for Technological Education of Minas Gerais (CEFET-MG), Department of Informatics, Management and Design, Campus Divinópolis, Rua Álvares de Azevedo, 400-Bela Vista, Divinópolis 35503-822, BrazilFederal Center for Technological Education of Minas Gerais (CEFET-MG), Department of Informatics, Management and Design, Campus Divinópolis, Rua Álvares de Azevedo, 400-Bela Vista, Divinópolis 35503-822, BrazilFederal Center for Technological Education of Minas Gerais (CEFET-MG), Department of Informatics, Management and Design, Campus Divinópolis, Rua Álvares de Azevedo, 400-Bela Vista, Divinópolis 35503-822, BrazilDepartment of Bioengineering, Federal University of São João del-Rei, Praça Dom Helvécio, 74-Fábricas, São João del-Rei 36301-1601, BrazilDrug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process.https://www.mdpi.com/2813-2998/2/2/17drug discoveryvirtual screeningmachine learningdeep learningCADDalgorithms |
| spellingShingle | Tiago Alves de Oliveira Michel Pires da Silva Eduardo Habib Bechelane Maia Alisson Marques da Silva Alex Gutterres Taranto Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods Drugs and Drug Candidates drug discovery virtual screening machine learning deep learning CADD algorithms |
| title | Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods |
| title_full | Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods |
| title_fullStr | Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods |
| title_full_unstemmed | Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods |
| title_short | Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods |
| title_sort | virtual screening algorithms in drug discovery a review focused on machine and deep learning methods |
| topic | drug discovery virtual screening machine learning deep learning CADD algorithms |
| url | https://www.mdpi.com/2813-2998/2/2/17 |
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