Augmenting DMTA using predictive AI modelling at AstraZeneca.

Journal: Drug discovery today
PMID: 38460568

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.

Authors

  • Gian Marco Ghiandoni
    Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom.
  • Emma Evertsson
    Research and Early Development, Respiratory and Immunology (R&I), Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden, Mölndal, SE 43183, Sweden.
  • David J Riley
    Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.
  • Christian Tyrchan
    Medicinal Chemistry, Early RIA, Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden.
  • Prakash Chandra Rathi
    Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals