Artificial intelligence (AI) techniques such as machine learning are transforming drug research and development (R&D), enabled by ever-increasing amounts of data and computational power. Historically, small molecules have been at the forefront of AI applications in drug discovery, including modelling small-molecule–target interactions, lead candidate optimization and safety prediction. However, AI tools are increasingly being applied to large-molecule modalities, including antibodies, gene therapies and RNA-based therapies. Such therapies represent an important share of the biopharma industry’s current portfolio — around
40% of new molecules approved in 2022 — and of its future commercial potential. For example, in oncology, large molecules are forecast to represent ~50% of the market by revenue in 2030, of which more than 80% is expected to be derived from antibodies.
