This is Part 2 of a three-part series on AI in drug discovery. Part 1 covered the molecular foundations: chemical space, molecular representations, graph neural networks, and transfer learning for QSAR modelling. Part 2 covers protein structure prediction, generative molecular design, and deep learning-powered virtual screening. Part 3 will examine clinical trial optimisation, multi-omics integration, and the open challenges facing the field. From Representing Molecules to Understanding Targets Part 1 established how machine learning models can learn to reason about small molecules: how chemical structures are encoded as SMILES strings, molecular graphs, or 3D conformers, and how graph neural networks trained on large molecular databases can predict biological activity from…