Workshop Primer on artificial intelligence (AI) for pharmaceutical scientists

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Denis DeBlois, Chair of the International Scientific Programme Commitee PSWC


Our ability to collect, store and analyze data is growing exponentially, creating new opportunities to understand and influence biological processes. The emerging field of AI has the potential to revolutionize pharmaceutical sciences, thanks to its ability to detect patterns in large data sets and learn from experience. This workshop is for all pharmaceutical scientists interested in learning how to best use big data and AI to support and accelerate their research. Primer presentations will be followed by a live interactive session with the experts to address issues encountered by pharmaceutical scientists in the fields of target and molecule discovery to precision medicine and optimization to drug usage using real-world evidence.




AI 101 – An introduction to the basics of AI and Machine Learning
Karim Jerbi, PhD, Université de Montréal, Director of the UNIQUE Center and member of Mila 

Target identification (the challenge of fat data)
François Laviolette, PhD, Université Laval and member of Mila 

Data-efficient drug design (virtual screening and optimal structure prediction)
Sébastien Giguère, PhD, member of IVADO and co-founder of InVivo AI

Health break

High resolution medicine (patient monitoring, profiling, real time analysis)
View slides here
Simon de Montigny, PhD, Université de Montréal

When are algorithmic models useful? (ML). Pharmacy-flavored Examples
View slides here
Steven Sanche, PhD, Los Alamos National Laboratory (USA) and Fahima Nekka, PhD, Université de Montréal

General discussion

This primer on AI is sponsored by FIP and IVADO, one of the world’s leading institutes in artificial intelligence and a pioneer in deep learning.

Learning Objectives

  1. What is AI and why is it a major advance in our ability to analyze data.
  2. What are the potential uses and limitations of AI at each steps of drug development.
  3. How can pharmaceutical scientists use the different kinds of AI in their research projects.



Target audience

Academic researchers, industry scientists and clinicians