Artificial intelligence (AI) in drug discovery is increasing. In this thematic research we examine the challenges facing drug discovery and development and explore the AI solution. AI is used across many biotech / pharmaceutical industry sub-sectors including drug discovery and development, drug repurposing, productivity, and clinical trials. The deployment of AI reduces costs, errors (and so risk) and speeds up the time to identifying target compounds. We examine some of the ways AI is and can help in a number of key aspects of the process of drug discovery and development and its future.
Digitalization is accelerating in the biotech and pharma sectors after something of a lag period. It is helping to solve complex clinical problems as the data sets multiply and deepen in complexity. The volume and level of complexity is driving biotech and pharma towards AI technology systems to perform some tasks that humans are less good at. We do not see the current capabilities of AI engines as anywhere close to being able to substitute for human intelligence.
However, AI is able to perform high volume repetitive tasks at speed and without human error and it can adapt based upon its algorithmic approach. This adaptation is often characterised as interpretation and learning without direct instruction. There is little doubt that the speed, reliability and relative independence with which AI can perform complex repetitive tasks will change the nature of work in the biotech and pharma sectors as it will elsewhere.