Identifying and accelerating drug development is big business. The costs in this industry are significant and finding pathways to optimize using AI methods is top of mind in this fast and evolving industry.
Deloitte found that the average cost of developing a new drug among the top 20 global biopharmas it studied rose 15% ($298 million) last year, to approximately $2.3 billion. That figure includes the average cost of developing a candidate from discovery through clinical trials to the market.
Many biopharmaceutical companies are using AI to speed up drug development. For example, machine-learning models are trained using information about the protein or amino-acid sequence or 3D structure of previous drug candidates, and about properties of interest.
Where can AI and machine learning add value?
Well it comes in a few areas. First AI can be used in drug development to speed up parts of the research process, helping reduce costs and improve efficiency. Research finds that AI can minimize the time taken to screen new drugs by as much as 40 to 50%, reducing the costs significantly.
Finding optimal ways to streamline end to end the entire drug development life cycle is a priority for every major player in the pharma R&D ecosystem and if they are not applying AI, they simply won’t be able to compete.
Source: Forbes
