The integration of artificial intelligence (AI) into drug discovery represents a paradigm shift in addressing the longstanding inefficiencies of traditional pipelines, which are characterized by substantial costs, extended timelines, and persistently low success rates. This presentation examines how AI methodologies can be systematically embedded across critical stages of the drug discovery workflow. We demonstrate that multiomics data integration and network-based computational approaches enable the identification of novel oncogenic vulnerabilities and the prioritization of high-confidence therapeutic targets. Structural prediction frameworks, exemplified by AlphaFold, have fundamentally advanced druggability assessments and structure-based drug design through accurate protein conformation modelling. AI-driven virtual screening and de novo molecular design further accelerate hit identification and lead optimization by generating chemically diverse structures with tailored biological properties. We additionally present how biophysical AI modelling is advancing antibody engineering by leveraging structural dynamics, molecular simulations, and deep learning to optimize binding affinity, specificity, and developability. We critically discuss current limitations, including training data bias, overfitting, and regulatory and ethical considerations surrounding data privacy.