Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. E...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10459028/ |
| _version_ | 1827308281103646720 |
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| author | Roohallah Alizadehsani Solomon Sunday Oyelere Sadiq Hussain Senthil Kumar Jagatheesaperumal Rene Ripardo Calixto Mohamed Rahouti Mohamad Roshanzamir Victor Hugo C. De Albuquerque |
| author_facet | Roohallah Alizadehsani Solomon Sunday Oyelere Sadiq Hussain Senthil Kumar Jagatheesaperumal Rene Ripardo Calixto Mohamed Rahouti Mohamad Roshanzamir Victor Hugo C. De Albuquerque |
| author_sort | Roohallah Alizadehsani |
| collection | DOAJ |
| description | The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field. |
| first_indexed | 2024-04-24T18:54:29Z |
| format | Article |
| id | doaj.art-de644cb0264842849d44b639b61d6134 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:29Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-de644cb0264842849d44b639b61d61342024-03-26T17:46:21ZengIEEEIEEE Access2169-35362024-01-0112357963581210.1109/ACCESS.2024.337319510459028Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive SurveyRoohallah Alizadehsani0https://orcid.org/0000-0002-3069-7932Solomon Sunday Oyelere1https://orcid.org/0000-0001-9895-6796Sadiq Hussain2https://orcid.org/0000-0002-9840-4796Senthil Kumar Jagatheesaperumal3https://orcid.org/0000-0002-9516-0327Rene Ripardo Calixto4Mohamed Rahouti5https://orcid.org/0000-0001-9701-5505Mohamad Roshanzamir6Victor Hugo C. De Albuquerque7https://orcid.org/0000-0003-3886-4309Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, AustraliaDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Skellefteå, SwedenExamination Branch, Dibrugarh University, Dibrugarh, Assam, IndiaDepartment of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, IndiaDepartment of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, BrazilDepartment of Computer and Information Science, Fordham University, Bronx, NY, USADepartment of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, IranDepartment of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, BrazilThe field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.https://ieeexplore.ieee.org/document/10459028/Drug discoveryexplainable artificial intelligencemachine learningbig data |
| spellingShingle | Roohallah Alizadehsani Solomon Sunday Oyelere Sadiq Hussain Senthil Kumar Jagatheesaperumal Rene Ripardo Calixto Mohamed Rahouti Mohamad Roshanzamir Victor Hugo C. De Albuquerque Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey IEEE Access Drug discovery explainable artificial intelligence machine learning big data |
| title | Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey |
| title_full | Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey |
| title_fullStr | Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey |
| title_full_unstemmed | Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey |
| title_short | Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey |
| title_sort | explainable artificial intelligence for drug discovery and development a comprehensive survey |
| topic | Drug discovery explainable artificial intelligence machine learning big data |
| url | https://ieeexplore.ieee.org/document/10459028/ |
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