Exclusive speaker interview released for SMi's AI in Drug Discovery Conference 2020

SMi Reports: University of Cambridge speaker to brief on Artificial Intelligence for AI in Drug Discovery 2020 taking place in London in March 2020.
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KENSINGTON, U.K. - Nov. 19, 2019 - PRLog -- Ahead of the two-day agenda, SMi has interviewed Andreas Bender, Lecturer for Molecular Informatics, University of Cambridge to discuss his view on the conference and what to expect from his presentation on 'Artificial Intelligence in Drug Discovery – Opportunities and Pitfalls'. Dr Andreas Bender is a Reader for Molecular Informatics with the Centre for Molecular Science Informatics at the Department of Chemistry of the University of Cambridge, leading a group of about 22 postdocs, PhD and graduate students and academic visitors.

Snapshot of Andreas' interview:

Can you tell us a little bit about your background in data-driven drug discovery informatics and your work in the field?

"I started working on cheminformatics in 1999, during the previous 'biotech boom', in a start up close to Berlin, did a PhD in the field in Cambridge. Afterwards I was happy to do an industrial postdoc with Novartis in Boston (where I learned about the complexity of biological high-content screening readouts), and for the last 12 years I have been a group leader in Leiden/The Netherlands, and Cambridge/UK. We work quite a lot with pharma and biotech companies and also started two companies ourselves - Healx and Pharmenable - so I would describe our work as quite applied."

In your opinion, how does AI improve drug discovery and what are the potential areas where AI would be applicable?

"AI depends on data, and data depends on labels. Mostly you can only (relatively) clearly label early stage data - say, binding to a target (but even here the question is whether binding sites, functional effects, or kinetics are a difference that matters or not)…"

For the full interview and speaker line-up, the brochure is available to download online. Register at www.AI-indrugdiscovery.com/prlog3

Key features for this year's conference include:

•         Explore how lead scientists from 10 big pharma apply machine learning methods to fast-track de novo design, synthesis and ADME toxicity predictions in aid of drug discovery

•         Listen to a panel discussion on the "AI paradigm shift – is it just a hype?" to find out what key opinion leaders think about the potential of AI for the future

•         Discover the unique approach using the AI-led Benevolent Platform® deploys for re-imagining the full discovery pipeline using AI

•         Deep dive into the latest case-studies from AstraZeneca exploring their strategies to integrating AI into drug design

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