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Explainable Artific...
Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey
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- Alizadehsani, Roohallah (författare)
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
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- Oyelere, Solomon Sunday (författare)
- Luleå tekniska universitet,Datavetenskap
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- Hussain, Sadiq (författare)
- Dibrugarh University, Examination Branch, Dibrugarh, Assam, India
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- Jagatheesaperumal, Senthil Kumar (författare)
- Mepco Schlenk Engineering College, Department of Electronics and Communication Engineering, Sivakasi, India
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- Calixto, Rene Ripardo (författare)
- Federal University of Ceará, Department of Teleinformatics Engineering, Fortaleza, Brazil
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- Rahouti, Mohamed (författare)
- Fordham University, Department of Computer and Information Science, Bronx, NY, USA
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- Roshanzamir, Mohamad (författare)
- Fasa University, Faculty of Engineering, Department of Computer Engineering, Fasa, Iran
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- De Albuquerque, Victor Hugo C. (författare)
- Federal University of Ceará, Department of Teleinformatics Engineering, Fortaleza, Brazil
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2024
- 2024
- Engelska.
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 12, s. 35796-35812
- Relaterad länk:
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- big data
- Drug discovery
- explainable artificial intelligence
- machine learning
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- for (ämneskategori)
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