Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
It provides a comprehensive overview for researchers and practitioners in drug discovery, but it is incremental as it synthesizes existing knowledge without introducing new methods or results.
This survey addresses the need for transparency in AI/ML models for drug discovery by reviewing Explainable Artificial Intelligence (XAI) methods, their applications in target identification, compound design, and toxicity prediction, and identifies challenges and future research directions to enhance interpretability in the field.
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. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.