This is the final part of a three-part series on AI in drug discovery. Part 1 covered molecular representations, graph neural networks, and transfer learning for QSAR modelling. Part 2 covered protein structure prediction with AlphaFold, generative molecular design, and deep learning virtual screening. Part 3 examines how AI is being applied beyond the molecule: to systems-level disease biology, clinical trial optimisation, and the open theoretical and practical challenges that remain. Beyond the Molecule Parts 1 and 2 of this series focused on AI in drug discovery at the molecular scale: representing chemical structures, predicting binding affinities, generating candidate molecules, and screening compound libraries computationally. These approaches operate primarily on…