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Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia.ORCID-id: 0000-0002-3069-7932
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-9895-6796
Dibrugarh University, Examination Branch, Dibrugarh, Assam, India.ORCID-id: 0000-0002-9840-4796
Mepco Schlenk Engineering College, Department of Electronics and Communication Engineering, Sivakasi, India.ORCID-id: 0000-0002-9516-0327
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2024 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 12, s. 35796-35812Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2024. Vol. 12, s. 35796-35812
Emneord [en]
big data, Drug discovery, explainable artificial intelligence, machine learning
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URN: urn:nbn:se:ltu:diva-104882DOI: 10.1109/ACCESS.2024.3373195Scopus ID: 2-s2.0-85187337752OAI: oai:DiVA.org:ltu-104882DiVA, id: diva2:1846965
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Validerad;2024;Nivå 2;2024-04-05 (marisr);

Full text license: CC BY

Tilgjengelig fra: 2024-03-26 Laget: 2024-03-26 Sist oppdatert: 2024-04-05bibliografisk kontrollert

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Oyelere, Solomon Sunday

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Alizadehsani, RoohallahOyelere, Solomon SundayHussain, SadiqJagatheesaperumal, Senthil KumarRahouti, MohamedDe Albuquerque, Victor Hugo C.
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