Transcript State of the art and is it used at Pfizer (Sandwich, UK)

State of the art and is it used at Pfizer (Sandwich, UK)
• Multi-objective “stuff”
± (we should do more)
• HTS/random screening
++ (standard practise)
• HT docking
- (has never worked for us, too slow)
• Pharmacophore generation
- (Other methods work better)
• Fragment based approaches
+ (Hot area but we are still learning)
• Machine learning / data mining
++ (Large part of my job)
• Focussed libraries
+ (Often done but design could be better)
• Pipeline Pilot
+++ (Changed our work dramatically)
• Other workflow tools (knime)
- (It is not free, cost of implementation)
• QSPR: property prediction
+ (Not by classic linear QSAR)
• Chemogenomics / chemical biology ++ (Hot area but very new to us)
• Bioisosters
+ (part of datamining effort)