Drug Discovery & Biotech

Drug development is one of the most expensive and failure-prone endeavours in any industry. It typically costs over a billion pounds and takes more than a decade to bring a single drug from initial discovery to approved treatment, with a failure rate above ninety per cent. AI is being applied at multiple stages of this process - identifying potential drug targets, predicting how molecules will interact with biological systems, optimising chemical compounds, and selecting patients for clinical trials. The promise is not that AI will replace pharmaceutical research but that it will dramatically reduce the time and cost of the early stages, where the search space is enormous and much of the work involves testing millions of possibilities. DeepMind's AlphaFold, which predicted protein structures that had stumped scientists for decades, demonstrated what AI can do when applied to the right biological problem. Several AI-driven drug candidates have entered clinical trials, though it is still early days for proving that AI-discovered drugs reach patients faster. The realistic expectation is that AI will shorten timelines, reduce costs, and increase the hit rate in early discovery - not that it will eliminate the need for rigorous clinical testing or regulatory approval. Those safeguards exist for good reason, and no amount of computational power should bypass them.