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  • 1.
    Alizadehsani, Roohallah
    et al.
    Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia.
    Oyelere, Solomon Sunday
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hussain, Sadiq
    Dibrugarh University, Examination Branch, Dibrugarh, Assam, India.
    Jagatheesaperumal, Senthil Kumar
    Mepco Schlenk Engineering College, Department of Electronics and Communication Engineering, Sivakasi, India.
    Calixto, Rene Ripardo
    Federal University of Ceará, Department of Teleinformatics Engineering, Fortaleza, Brazil.
    Rahouti, Mohamed
    Fordham University, Department of Computer and Information Science, Bronx, NY, USA.
    Roshanzamir, Mohamad
    Fasa University, Faculty of Engineering, Department of Computer Engineering, Fasa, Iran.
    De Albuquerque, Victor Hugo C.
    Federal University of Ceará, Department of Teleinformatics Engineering, Fortaleza, Brazil.
    Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey2024In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 35796-35812Article, review/survey (Refereed)
    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.

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