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
Statistical Science Director @ AstraZeneca
David holds a PhD in Mathematical Statistics from the Chalmers University of Technology in Sweden. He joined AstraZeneca in Gothenburg in 2003 and is now a Statistical Science Director in the Statistical Innovation group within the Advanced Analytic Centre. In that position, he is both involved in directly supporting late phase clinical drug projects and methodological longer-term work (e.g. relating to biomarkers/subgroups, personalized medicine, machine learning, and safety). He is particularly interested in computationally intensive statistics, machine learning, visualisation, and Bayesian approaches, and he is a keen R user. The lead of PSI Special Interest Group on Subgroup Analysis since 2018.