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UID:20251106T003756EST-1379ulwuhE@132.216.98.100
DTSTAMP:20251106T053756Z
DESCRIPTION:Hugh Chipman\, Canada Research Chair in Mathematical Modeling\,
  Dept of Mathematics and Statistics\, Acadia University\, Wolfville\, N.S.
  High-throughput screening of compounds for biological activity is often a
 n important first step in the drug discovery process. From a statistical l
 earning perspective\, the results of screening process can be used to cons
 truct a model. Using various descriptors of molecular structure as inputs\
 , we seek to predict activity.  These descriptors can be easily calculated
 \, but the activity is the outcome of more expensive screening procedures.
   Screening results for a part of the library constitute a training set\, 
 which can be used to build a model to predict activity. This model enables
  'virtual screening' in which activity is predicted rather than measured. 
 This talk describes a number of recent models developed for such virtual s
 creening\, including mixture discriminant analysis\, decision trees\, near
 est neighbours\, and ensemble models.\n
DTSTART:20071024T193000Z
DTEND:20071024T203000Z
LOCATION:Duff Medical Building\, CA\, QC\, Montreal\, H3A 2B4\, 3775 rue Un
 iversity
SUMMARY:Statistical learning and virtual screening in drug discovery
URL:/channels/event/statistical-learning-and-virtual-s
 creening-drug-discovery-27610
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