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Explainable Artificial Intelligence for Drug Discovery and Development - A Comprehensive Survey

journal contribution
posted on 2024-03-27, 02:43 authored by Roohallah AlizadehsaniRoohallah Alizadehsani, SS Oyelere, S Hussain, SK Jagatheesaperumal, RR Calixto, M Rahouti, M Roshanzamir, VHC De Albuquerque
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.

History

Journal

IEEE Access

Volume

12

Pagination

35796-35812

Location

Piscataway, N.J.

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

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