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* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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)