Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery
The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/16/3/332 |
| _version_ | 1797609575271104512 |
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| author | Samuel K. Kwofie Joseph Adams Emmanuel Broni Kweku S. Enninful Clement Agoni Mahmoud E. S. Soliman Michael D. Wilson |
| author_facet | Samuel K. Kwofie Joseph Adams Emmanuel Broni Kweku S. Enninful Clement Agoni Mahmoud E. S. Soliman Michael D. Wilson |
| author_sort | Samuel K. Kwofie |
| collection | DOAJ |
| description | The effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline. |
| first_indexed | 2024-03-11T06:03:18Z |
| format | Article |
| id | doaj.art-8012805661244f51a4e443eb0705e0eb |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-11T06:03:18Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-8012805661244f51a4e443eb0705e0eb2023-11-17T13:11:19ZengMDPI AGPharmaceuticals1424-82472023-02-0116333210.3390/ph16030332Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug DiscoverySamuel K. Kwofie0Joseph Adams1Emmanuel Broni2Kweku S. Enninful3Clement Agoni4Mahmoud E. S. Soliman5Michael D. Wilson6Department of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Accra P.O. Box LG 77, GhanaDepartment of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra P.O. Box LG 581, GhanaDepartment of Biomedical Engineering, School of Engineering Sciences, College of Basic and Applied Sciences, University of Ghana, Accra P.O. Box LG 77, GhanaDepartment of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra P.O. Box LG 581, GhanaDiscipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South AfricaDiscipline of Pharmaceutical Sciences, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South AfricaDepartment of Parasitology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra P.O. Box LG 581, GhanaThe effect of Ebola virus disease (EVD) is fatal and devastating, necessitating several efforts to identify potent biotherapeutic molecules. This review seeks to provide perspectives on complementing existing work on Ebola virus (EBOV) by discussing the role of machine learning (ML) techniques in the prediction of small molecule inhibitors of EBOV. Different ML algorithms have been used to predict anti-EBOV compounds, including Bayesian, support vector machine, and random forest algorithms, which present strong models with credible outcomes. The use of deep learning models for predicting anti-EBOV molecules is underutilized; therefore, we discuss how such models could be leveraged to develop fast, efficient, robust, and novel algorithms to aid in the discovery of anti-EBOV drugs. We further discuss the deep neural network as a plausible ML algorithm for predicting anti-EBOV compounds. We also summarize the plethora of data sources necessary for ML predictions in the form of systematic and comprehensive high-dimensional data. With ongoing efforts to eradicate EVD, the application of artificial intelligence-based ML to EBOV drug discovery research can promote data-driven decision making and may help to reduce the high attrition rates of compounds in the drug development pipeline.https://www.mdpi.com/1424-8247/16/3/332drug discoverydeep learningartificial intelligencebig dataEbola virusclassifiers |
| spellingShingle | Samuel K. Kwofie Joseph Adams Emmanuel Broni Kweku S. Enninful Clement Agoni Mahmoud E. S. Soliman Michael D. Wilson Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery Pharmaceuticals drug discovery deep learning artificial intelligence big data Ebola virus classifiers |
| title | Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery |
| title_full | Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery |
| title_fullStr | Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery |
| title_full_unstemmed | Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery |
| title_short | Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery |
| title_sort | artificial intelligence machine learning and big data for ebola virus drug discovery |
| topic | drug discovery deep learning artificial intelligence big data Ebola virus classifiers |
| url | https://www.mdpi.com/1424-8247/16/3/332 |
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