DIGITAL HEALTHCARE
It’ s fair to say AI is reshaping life sciences, with its impact on drug discovery and development moving from promise to proven value.
From accelerating timelines to improving the quality of drug candidates, emerging capabilities are unlocking new possibilities across the pharmaceutical R & D pipeline. Drawing on Capgemini’ s latest research and real-world examples, Thorsten Rall, Global Life Sciences Leader at Capgemini, explores where AI is already delivering tangible results – from target identification and silico experimentation to clinical trial optimisation – and where its longer-term potential lies.
Q. WHICH ELEMENTS OF DRUG DISCOVERY WILL AI TRANSFORM MOST SIGNIFICANTLY?
» For me, the biggest opportunities are in target discovery, hit and lead prioritisation, and in silico experimentation that can reduce reliance on some forms of laboratory and animal testing. And this is supported by what we see in our recent Capgemini Research Institute report where 63 % of biopharma executives believe the majority of new molecular entities( NMEs) will originate from AI-driven platforms within the next decade.
AI can certainly accelerate parts of the discovery process, but I think the more significant impact is on quality. Historically, drug discovery has involved screening enormous numbers of potential drug candidate molecules and accepting a high degree of failure.
AI gives us the ability to identify more promising targets faster and design candidate molecules far more efficiently.
For example, Insilico Medicine’ s average timeline to nominate a preclinical candidate is between nine and 12 months vs the industry average of four to six years. Increasingly, the biopharma industry is seeing improved probability of success in phase one trials indicating higher quality candidates are coming through from AI.
100 August 2026