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SUMMARY:Artificial Intelligence in Drug Discovery and Computational Biolog
 y: Current Status\, Successes\, and Pitfalls - Andreas Bender (University 
 of Cambridge)
DTSTART:20250306T160000Z
DTEND:20250306T170000Z
UID:TALK227122@talks.cam.ac.uk
CONTACT:Michael Boemo
DESCRIPTION:The amount of chemical and biological data available has incre
 ased in the public as well as the private domain\, and both on the algorit
 hmic and hardware side progress has been tremendous in machine learning. P
 ress releases describe the design of functional proteins and antibodies fr
 om scratch\, and several ‘first AI-designed drugs’ have already entere
 d clinical phases.\n\nHowever\, all is not well when it comes to the marri
 age of algorithms with drug discovery\, in particular when it comes to the
  in vivo relevance of what we are able to do with chemical and biological 
 data at this point in time. Reasons for this are that the field is still s
 tuck in reductionist thinking\, in combination with a lack of relevant dat
 a and our ability to handle it computationally to support decision making.
 \n\nThis contribution will review the current status of the field\, as wel
 l as provide case studies where data and computational methods have been a
 ble to select compounds with the desired effects on a biological system\, 
 and explain what currently still hampers further progress.
LOCATION:Lecture Theatre\, Department of Pathology\, Tennis Court Road
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