# Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer
###### Abstract  
Drug discovery and development remains a complex and time-consuming process, often hindered by high costs and low success rates. In the big data era, artificial intelligence (AI) has emerged as a promising tool to accelerate and optimize these processes, particularly in the field of oncology. This review explores the application of AI-based methods for drug repurposing and natural product-inspired drug design in cancer, focusing on their potential to address the challenges and limitations of traditional drug discovery approaches. We delve into various AI-based approaches (machine learning, deep learning, and others) that are currently being employed for these purposes, and the role of experimental techniques in these approaches. By systematically reviewing the literature, we aim to provide a comprehensive overview of the current state of AI-assisted cancer drug discovery workflows, highlighting AI's contributions to accelerating drug development, reducing costs, and improving therapeutic outcomes. This review also discusses the challenges and opportunities associated with the integration of AI into the drug discovery pipeline, such as data quality, interpretability, and ethical considerations. drug repurposing; artificial intelligence; machine learning; cancer +
Footnote †: journal: Applied sciences  
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Footnote 0: _Copyright_: 02025  
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challenges are not only financial, but also involve overcoming numerous scientific and logistical hurdles, which can delay progress and increase costs. Despite notable advances in technology and methodology, the drug development process continues to face significant impediments, particularly in the form of high clinical trial failure rates. These failures are primarily attributed to ongoing concerns regarding the safety and efficacy of compounds. While efforts to optimize the physicochemical properties of compounds have yielded some benefits, ensuring the safety and efficacy of novel therapeutics remains a critical challenge that the industry must address [2]. This challenge is further exacerbated in the context of complex diseases such as cancer. Cancer is characterized by diverse genetic and molecular heterogeneity, which complicates the development of effective treatments. The variability in genetic profiles and molecular pathways among different cancer types and even among patients with the same type of cancer adds layers of complexity to the drug development process [3]. Consequently, given the severity and high global incidence of cancer, there is an urgent need to prioritize the search for new strategies to optimize drug discovery. This priority is driven by the necessity to develop more effective and targeted therapies that can address the unique challenges posed by cancer's heterogeneity. The integration of innovative technologies and approaches, such as artificial intelligence and machine learning, into the drug discovery process holds promise for overcoming some of these challenges by enabling more precise targeting of therapies and improving the efficiency of the development pipeline. In this context, artificial intelligence (AI) has emerged as a transformative tool with great potential to significantly impact drug discovery. Through techniques such as machine learning (including convolutional neural networks [CNNs] and recurrent neural networks [RNNs]), deep learning, and evolutionary algorithms, AI makes it possible to analyze extensive biomedical data, predict molecular interactions, and optimize drug development with unparalleled accuracy, leading to significant reductions in time and costs [4]. These technologies also facilitate the identification of potential drug candidates, the prediction of their effects, and the tailoring of treatments to individual patients, enabling personalized medicine [5] (Figure 1).