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Augmenting DMTA using predictive AI modelling at AstraZeneca.

Gian Marco Ghiandoni1, Emma Evertsson2, David J Riley1

  • 1Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.

Drug Discovery Today
|March 9, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and cloud computing streamline drug discovery. AstraZeneca's Predictive Insight Platform (PIP) accelerates the Design-Make-Test-Analyse cycle, reducing the iterations needed for identifying viable drug candidates.

Keywords:
artificial intelligencecloud computingdrug discoverymachine learning

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Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Pharmaceutical sciences

Background:

  • The traditional Design-Make-Test-Analyse (DMTA) cycle is iterative and can require numerous cycles to identify viable drug candidates.
  • Advancements in artificial intelligence (AI) and cloud computing offer potential to optimize and accelerate the drug discovery process.

Purpose of the Study:

  • To introduce the Predictive Insight Platform (PIP), a novel cloud-native modeling platform developed at AstraZeneca.
  • To discuss the impact, architecture, integration, and usage of PIP within the DMTA framework.
  • To provide insights into the future of AI-driven drug discovery.

Main Methods:

  • Development of a cloud-native modeling platform (PIP).
  • Integration of PIP into the Design-Make-Test-Analyse (DMTA) workflow.
  • Analysis of PIP's impact on each stage of the DMTA cycle.

Main Results:

  • PIP enhances efficiency across all stages of the DMTA cycle.
  • The platform's architecture and integration facilitate seamless data flow and analysis.
  • PIP demonstrates the potential to significantly reduce the number of cycles required for drug candidate identification.

Conclusions:

  • The Predictive Insight Platform (PIP) represents a significant advancement in AI-powered drug discovery.
  • PIP's application within the DMTA cycle accelerates the identification of viable drug candidates.
  • The platform offers valuable insights into the future trajectory of pharmaceutical research and development.