Augmenting DMTA using predictive AI modelling at AstraZeneca

Abstract

Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery. Teaser: This review of the role, impact, and architecture of AstraZeneca’s Predictive Insight Platform (PIP), a cloud-native modelling platform that aims to accelerate drug discovery, offers perspective on the evolution of R&D in pharma.