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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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Esben Jannik Bjerrum

Principal Scientist @ AstraZeneca

I want to battle diseases by inventing novel medicines. To accomplish this, I develop AI and ML-based methods and apply them to drug discovery projects. I obtained a PhD in computational chemistry from the Danish University of Pharmaceutical Sciences in 2008. I have since worked in the interface between IT, drug discovery, and research, both in industry, as a post.doc., and as an independent consultant. Over the years, I have gotten increasingly involved in machine learning and lastly artificial neural networks and deep learning for chemical applications. I joined AstraZeneca in late 2018.