Machine Learning Methods in Drug Discovery
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. Th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/25/22/5277 |
| _version_ | 1797548121107988480 |
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| author | Lauv Patel Tripti Shukla Xiuzhen Huang David W. Ussery Shanzhi Wang |
| author_facet | Lauv Patel Tripti Shukla Xiuzhen Huang David W. Ussery Shanzhi Wang |
| author_sort | Lauv Patel |
| collection | DOAJ |
| description | The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed. |
| first_indexed | 2024-03-10T14:55:00Z |
| format | Article |
| id | doaj.art-7502f06ebfe945a1af52f87ba3a6e1dd |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T14:55:00Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-7502f06ebfe945a1af52f87ba3a6e1dd2023-11-20T20:42:59ZengMDPI AGMolecules1420-30492020-11-012522527710.3390/molecules25225277Machine Learning Methods in Drug DiscoveryLauv Patel0Tripti Shukla1Xiuzhen Huang2David W. Ussery3Shanzhi Wang4Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USAChemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USADepartment of Computer Science, Arkansas State University, Jonesboro, AR 72467, USADepartment of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USAChemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USAThe advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.https://www.mdpi.com/1420-3049/25/22/5277machine learningdrug discoverydeep learningin silico screening |
| spellingShingle | Lauv Patel Tripti Shukla Xiuzhen Huang David W. Ussery Shanzhi Wang Machine Learning Methods in Drug Discovery Molecules machine learning drug discovery deep learning in silico screening |
| title | Machine Learning Methods in Drug Discovery |
| title_full | Machine Learning Methods in Drug Discovery |
| title_fullStr | Machine Learning Methods in Drug Discovery |
| title_full_unstemmed | Machine Learning Methods in Drug Discovery |
| title_short | Machine Learning Methods in Drug Discovery |
| title_sort | machine learning methods in drug discovery |
| topic | machine learning drug discovery deep learning in silico screening |
| url | https://www.mdpi.com/1420-3049/25/22/5277 |
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