The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies
Alexandre Blanco-Gonzalez, Alfonso Cabezon, Alejandro Seco-Gonzalez,, Daniel Conde-Torres, Paula Antelo-Riveiro, Angel Pineiro, Rebeca, Garcia-Fandino

TL;DR
This review explores how AI can transform drug discovery by enhancing efficiency and accuracy, discusses current challenges like data quality and ethics, and proposes strategies such as explainable AI and data augmentation to overcome obstacles.
Contribution
It provides a comprehensive overview of AI's potential in drug discovery, highlighting strategies to address existing challenges and integrating AI with traditional methods.
Findings
AI can significantly accelerate drug discovery processes
Data quality and ethical issues are major challenges for AI in this field
Strategies like explainable AI and data augmentation can mitigate current obstacles
Abstract
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note…
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Taxonomy
TopicsMachine Learning in Materials Science · Computational Drug Discovery Methods · Innovative Microfluidic and Catalytic Techniques Innovation
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