# Artificial Intelligence-Based Methods for Drug Repurposing and Development in Cancer
###### Abstract
For example, by using dynamic computational models of the pathophysiological processes derived from drugs, it has been possible to achieve personalized immunotherapy in cancer patients. AI also plays a fundamental role in the analysis of omics data mining, particularly in the context of integrating genomic information from next-generation sequencing of thousands of individuals with clinical data and predictive computational models. This integration has been demonstrated to represent a significant advancement in the automation of the research and identification of novel therapeutic targets. Omics technologies also facilitate drug repositioning by identifying new therapeutic uses for existing drugs, accelerating the process and reducing attrition rates in development and clinical trials. This is primarily based on analyzing large datasets to identify patterns and relationships between drugs and diseases through computational modeling [6]. Consequently, multi-omics technologies, in conjunction with machine learning (ML) and deep learning (DL), have facilitated substantial progress in data integration. This is attributable to their substantial processing capacity for both linear and non-linear data, thereby establishing a linkage between genomic alterations and specific therapeutic options for patients. However, integration tools have not demonstrated significant performance since they use data generated from multiple databases, which has not allowed for standardized databases, but it is estimated that these challenges can be solved with AI approaches and standardization [7; 8]. Furthermore, advances in AI are not only helping to develop more effective targeted therapies but also to predict patient response. For example, AI-based algorithms can identify subgroups of patientslikely to respond to specific immunotherapies or combination treatments [4]. AI also plays an increasingly significant role in optimizing polypharmacology strategies and predicting drug side effects. Machine learning models can predict potential side effects even for new substances by analyzing databases of adverse events and drug-protein interactions [9; 10]. AI-driven methods pave the way for designing multi-target medications, maximizing treatment effectiveness and reducing side effects [11]. Both strategies contribute to efficient drug development and reduce late-stage clinical trial failures by integrating diverse datasets (Figure 1). In the domain of cancer research, AI plays a critical role in identifying biomarkers and therapeutic targets. Deep learning and machine learning models facilitate the analysis of thousands of data points, such as genomic and transcriptomic data, to identify key mutations and molecular alterations driving tumor growth [6]. Integrating these data with clinical information (patient history) can provide insights into the efficacy of different treatments for specific genetic profiles. The field has been revolutionized by tools such as AlphaFold, which can predict protein structures with a high degree of accuracy, thus providing essential information for the design of targeted drugs [12]. The integration of AI with multi-omics technologies signifies a paradigm shift in precision oncology. Integrative multi-omics data methods utilize genomic, transcriptomic, epigenomic, and proteomic information to identify crucial interactions between genes, proteins, and molecular pathways [13]. This integration facilitates the design of combined therapies specific to tumor subtypes and accelerates the identification of optimal drug combinations using neural networks and generative approaches, thereby optimizing therapeutic efficacy and minimizing side effects [13; 14]. In addition, advances in deep learning have proven effective in predicting the binding affinity between drugs and proteins, a crucial step in the drug discovery and development process for cancer [15]. Figure 1: The evolving landscape of AI-assisted drug discovery.