# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 7 The Role of AI in Cancer Genomics
### Drug Repurposing: A Cost-Effective Strategy Enhanced by Artificial Intelligence  
The process of developing a new anti-cancer drug from the beginning is extremely time-consuming and expensive, often spanning 10-15 years and costing over USD 1 billion from initial discovery to clinical approval [88]. Drug repurposing offers an excellent solution to this problem by identifying new therapeutic uses for existing drugs, bypassing early-stage development obstacles. In oncology, drug repurposing can significantly accelerate the availability of treatments because known drugs have established safety profiles, allowing researchers to move directly into Phase II or III trials for efficacy in cancer. Historically, drugs like thalidomide (now used in multiple myeloma) and metformin (investigated in breast and ovarian cancer) have had successful repurposing applications [89]. Many other non-oncological drugs, such as antivirals, antiparasities, or metabolic agents, have been investigated for anti-cancer properties, potentially expanding the arsenal of cancer therapies without the need to design a molecule from the ground up. This strategy can substantially reduce development costs and timelines, and it leverages the wealth of clinical data already available for approved medications. AI has emerged as a key promoter of drug repurposing by rapidly processing and interpreting vast biomedical datasets. ML and DL algorithms can integrate transcriptomics, proteomics, and chemical structure data to predict novel drug-target interactions. For instance, models like AtomNet and Pafnucy use 3D convolutional neural networks to enhance molecular docking predictions, revealing binding affinities of existing drugs for new cancer targets [90]. These systems outperform traditional methods by learning directly from real drug-target interaction data, achieving higher accuracy in predicting binding affinities (with AtomNet reporting AUC values around 0.85-0.90 compared to below 0.7 for  
Figure 3: Multimodal data integration in cancer genomics. Clinical, pathological, radiological, and genomic data are processed to extract relevant insights, which AI then leverages to assist in diagnosis, prognosis, treatment selection, and disease monitoring. classical docking) and reducing false positives in virtual screening [91]. Pafnucy similarly improves binding affinity prediction accuracy, showing significant reductions in error metrics compared to conventional scoring functions, thus enabling more reliable and efficient drug discovery pipelines [92]. Additionally, transcriptomic signature matching has been widely adopted. This involves identifying drugs that reverse disease-specific gene expression patterns. This approach compares gene expression profiles induced by disease states with those induced by drugs, identifying compounds capable of functionally reversing oncogenic transcriptional programs. Tools like CMap and algorithms like DrInsight or DeCoST have uncovered compounds with the potential to modulate key cancer-related signatures, particularly in hard-to-treat subtypes like triple-negative breast cancer [93]. Notably, drugs like thioridazine and lovastatin have shown efficacy in targeting cancer stem cell traits or estrogen receptor activity through these in silico techniques. Table 6 summarizes additional examples of repurposed drugs with established or emerging oncological applications, along with their mechanisms of action. Beyond computational approaches, high-throughput screenings, organoid models, and phenotypic analyses (e.g., filopodia inhibition assays) offer complementary experimental strategies for identifying repurposing candidates. For example, tumoroid models allow for preclinical validation of repurposed drugs within personalized tumor microenvironments [99]. Overall, drug repurposing, especially when coupled with AI, offers a powerful, cost-effective, and time-saving opportunity for expanding oncological treatment options.