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Related Concept Videos

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Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform.

Mickaël Guedj1, Jack Swindle2, Antoine Hamon2

  • 1Servier, Research & Development, Suresnes, France.

Expert Opinion on Drug Discovery
|July 5, 2022
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Summary

A pharmaceutical company developed the Patrimony high-throughput computing platform to accelerate drug discovery using artificial intelligence and proprietary data. This computational approach enhances R&D efficiency and competitiveness in identifying therapeutic targets.

Keywords:
Computational Precision MedicineDrug discoveryartificial intelligencecomputing platformdata integrationmulti-omicstarget identification

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Area of Science:

  • Computational drug discovery
  • Pharmaceutical research and development
  • Precision medicine

Background:

  • A mid-size international pharmaceutical company launched a high-throughput computing platform, 'Patrimony', four years ago.
  • The platform leverages proprietary and public data sources to support drug discovery initiatives.
  • It aims to foster a Computational Precision Medicine approach powered by artificial intelligence.

Purpose of the Study:

  • To identify novel therapeutic target candidates using the Patrimony platform.
  • To document the transformational impact of this industrial computational platform on R&D.
  • To demonstrate how the platform enhances competitiveness, saves time, and reduces costs in drug discovery.

Main Methods:

  • Development and implementation of a high-throughput computing platform ('Patrimony').
  • Integration of proprietary and public data sources.
  • Application of artificial intelligence and model-based selection for therapeutic targets and drug candidates.

Main Results:

  • Successful identification of novel therapeutic targets in immuno-inflammatory diseases.
  • Ongoing expansion of applications to oncology and neurology.
  • Demonstrated improvements in R&D competitiveness, time, and cost-effectiveness.

Conclusions:

  • The Patrimony platform has had a significant impact on the company's R&D processes.
  • Model-based selection of targets and drug candidates leads to more efficient drug discovery.
  • Challenges in data governance, infrastructure, and user adoption were encountered but addressed.