This is Part 1 of a three-part series on AI in drug discovery. Part 1 covers the theoretical foundations: the drug discovery pipeline, molecular representation, and how machine learning models learn to reason about chemical space. Part 2 will cover protein structure prediction, generative molecular design, and virtual screening. Part 3 will examine clinical trial optimisation, multi-omics integration, and the open challenges facing the field. A Pipeline in Crisis The pharmaceutical industry operates under a brutal set of statistics. It takes an average of 12 to 15 years and over $2 billion to bring a single new drug from initial discovery to regulatory approval. Roughly 90% of drug candidates that…