Transforming Precision Medicine through Generative AI: Advanced Architectures and Tailored Therapeutic Design for Patient‐Specific Drug Discovery

Abstract Generative AI is redefining precision medicine by enabling the rational design of patient‐specific therapeutics and overcoming the inherent inefficiencies of conventional drug discovery workflows, including protracted timelines, high attrition rates, and prohibitive R&D expenditures. Generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), transformer‐based architectures, and denoising diffusion models (DDMs) enable de novo molecular generation guided by multi‐omics datasets (genomics, transcriptomics, proteomics), facilitating the design of small molecules, peptides, and biologics tailored to individual molecular profiles. These architectures operate in high‐dimensional latent chemical spaces, allowing chemical morphing, scaffold hopping, and latent space optimization to enhance potency, selectivity, and ADME/Tox properties. Integration with QSAR models, molecular docking, molecular dynamics simulations, and protein–ligand binding affinity predictors strengthens the accuracy of drug–target interaction profiling. Reinforcement learning (RL) and graph neural network (GNN)‐based generative models further optimize lead compounds through iterative reward‐driven refinement, while SE(3)‐equivariant neural networks enable faithful 3D molecular generation and conformational stability predictions. Despite algorithmic advances, experimental validation remains indispensable to address inter‐patient metabolic heterogeneity, polypharmacology, and off‐target liabilities. Nevertheless, the convergence of generative AI with multi‐omics and high‐throughput screening platforms accelerates personalized drug discovery pipelines, establishing a paradigm shift toward precision, scalability, and translational efficiency.