AI drug discovery meets tokenised IP

Drug development has traditionally been financed as a long-duration, high-risk bet. A promising target can require years of laboratory work, clinical trials and regulatory review before investors receive a meaningful signal about whether the science will work. The Congressional Budget Office cites estimates of average R&D spending per successful new drug ranging from roughly $0.8 billion to $2.3 billion, once failed projects and the cost of capital are included. Two technologies are now attacking different areas of that cost structure: AI can reduce the scientific friction by helping identify targets, generate molecules, analyses datasets and prioritise experiments; tokenisation can reduce financing and ownership friction by allowing rights in research, intellectual property and future economics to be represented digitally, funded in smaller denominations and potentially repriced as milestones are reached. Neither development proves that medicines will suddenly become cheap since clin…