Exploring deep learning approaches in anticancer drug design: a review of recent advances

Abstract

Anticancer drug design plays a critical role in developing targeted therapies to combat the complexity and heterogeneity of cancer, a leading cause of mortality worldwide. However, the process of discovering and optimizing anticancer drugs is fraught with challenges, including the need to account for genetic variability, drug resistance, and off-target effects. Traditional methods, such as high-throughput screening and structure-based drug design, have advanced the field but often face limitations due to their computational cost, time-intensive nature, and inability to fully capture the dynamic nature of cancer biology. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have revolutionized drug design, including anticancer drug design, by enabling the analysis of complex biological data, prediction of drug-target interactions, and generation of novel therapeutic compounds. This article provides a comprehensive review of recent advances in anticancer drug design, with a focus on the transformative role of deep learning. While numerous studies have explored deep learning applications in general drug design, specific research focusing on anticancer drug development remains limited. In this context, we highlight the importance of optimizing chemical properties to transform generated molecules into effective therapeutic candidates. Furthermore, real-world applications are examined, and both challenges and future research opportunities are discussed to guide the development of more precise and personalized approaches to anticancer drug discovery.

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