Pharma's spend on AI in drug discovery 'could top $3bn by 2025'

Scientist Working in The Laboratory

The pharmaceutical industry is increasingly relying on artificial intelligence to power its drug discovery and development efforts, and its spend in this area has created a multibillion-dollar market for AI technologies.

That's the conclusion of a GlobalData report, which notes that AI is being used to enhance computer-aided drug design (CADD) in a bid to reduce the time and costs involved in getting a new drug to market.

That spend is due to reach of $3 billion by 2025, according to GlobalData analyst Kitty Whitney, who notes that there has been a steady stream of startups formed in the last three to four years to tap into the emerging AI market.

The number of AI-based drug discovery strategic alliances has increased significantly, from just 10 in 2015 to 105 in 2021, with companies like BenevolentAI, Exscientia and Insilico Medicine featuring prominently in the dealmaking.

The aim is to overcome a very low success rate in drug discovery, with just 10% of candidates making it into clinical development despite the application of new computational technology techniques to handle an ever-growing amount of biomedical data, for example from genomics studies.

As it stands, the time needed for a drug to reach the market ranges from 12 to 18 years, with an average cost of about $2.6 billion, according to the report.

"Drug discovery processes involve target identification and validation, assay development and screening, hit identification, lead optimisation, and the selection of candidates for further clinical development," said Whitney.

"The overall process takes several months and often results in low hit rates or poor-quality hits. AI has shown enormous potential to further enhance these methods by rapidly ingesting and exploring the expanding chemical space," she added.

AI is being increasingly used to enhance CADD methods, for example in identifying drug targets, virtual screening of compounds, de novo drug design, drug repurposing, and identification of treatment response biomarkers.

The category is facing challenges however, such as the quality and appropriateness of data, educating the scientific community to increase buy-in, overcoming the hype or mainstream narrative about AI, and skills shortages.

Among the pharma companies most active in adopting AI are Janssen, AstraZeneca, Pfizer, Bayer, Bristol Myers Squibb, GSK, Sanofi, and Takeda, according to the report.