6533b834fe1ef96bd129d471
RESEARCH PRODUCT
Improving Pattern Recognition Based Pharmacological Drug Selection Through ROC Analysis
F. PérezM. MurciaWladimiro DíazF.j. FerriMaría José Castrosubject
DrugReceiver operating characteristicCombinatorial Chemistry TechniquesComputer sciencebusiness.industryProcess (engineering)media_common.quotation_subjectMachine learningcomputer.software_genrePattern recognition (psychology)Artificial intelligencebusinesscomputerSelection (genetic algorithm)media_commondescription
The design of new medical drugs is a very complex process in which combinatorial chemistry techniques are used. The goal consists of discriminating between molecular compounds exhibiting or not certain pharmacological activities. Different machine learning approaches have been recently applied to different drug design problems leading to competitive results in pointing at particular compounds with high probability of exhibiting activity. The present work first deeps into the natural trade-off between accuracy in the much less populated active group and false alarm rate which could lead to too many expensive laboratory tests. Preliminary results show how different classification techniques are suited for this particular problem and throw light to keep improving the results by considering also the acceptance/rejection trade-off.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2004-01-01 |