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August 26, 2025Open Access

Artificial intelligence and machine learning in drug discovery-a review

Key Points

AI and ML enhance drug discovery efficiency, making the process faster and more cost-effective than traditional methods.
Using these technologies, researchers can quickly identify potential drug candidates and target molecules, leading to quicker development cycles.
The review explores challenges facing AI integration, such as data quality and regulatory issues, which may hinder widespread adoption.
Insights into future trends suggest that AI will continue to reshape the pharmaceutical landscape, improving drug safety and efficacy.

Abstract

The emergence of artificial intelligence (AI) and machine learning (ML) has revolutionized various sectors, including healthcare and pharmaceutical industries. Among the most transformative applications is their role in drug discovery and development. Traditionally a costly, laborious, and time-consuming process, drug discovery has significantly benefited from the integration of AI and ML technologies, enabling rapid identification of potential drug candidates, target identification, drug repurposing, and prediction of pharmacokinetics and toxicity. This article explores the principles, applications, benefits, challenges, and future perspectives of AI and ML in drug discovery, supported by contemporary references.

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