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Better, faster, and cheaper drug discovery with machine learning

  • Lindholmen Conference Hall 5 Lindholmspiren Västra Götalands län, 417 56 Sweden (map)

Abstract

AstraZeneca is finding new and innovative ways to use AI to help solve some of the biggest challenges facing the pharmaceutical industry today. To become better, faster, and cheaper in drug discovery and development, we believe in our AI approaches at AstraZeneca to transform R&D. Please join our session to get an overview of some of the real use-cases where AI is having a genuine impact across the R&D value chain:

  • Machine learning to predict compound properties to minimize the number of compounds made and tested

  • Methods to identify and improve the safety profile of new drugs as well as reduce the costs and time to bring these to the clinic

  • AI approaches for discovering patients responding better to treatment

  • Designing Molecules using Recurrent Neural Networks and Reinforcement Learning

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Emma Evertsson

Associate Principal Scientist Computational Chemistry @ AstraZeneca

Emma Evertsson is a computational chemist with a PhD in theoretical chemistry from Lund University. At AstraZeneca, she is involved in drug design for the treatment of respiratory diseases. In addition, she is responsible for the computational platform providing predicted property values for real and virtual molecules from machine learning models. This platform is intended to reduce the number of experiments and to accelerate the drug development process.