6533b856fe1ef96bd12b2fb2
RESEARCH PRODUCT
Drug Activity Characterization Using One-Class Support Vector Machines with Counterexamples
Wladimiro Diaz-villanuevaCarlos MorellFrancesc J. FerriAlicia Hurtado-corteganasubject
Chemical activitybusiness.industryCharacterization (mathematics)Machine learningcomputer.software_genreClass (biology)Task (project management)Support vector machineDrug activityBinary classificationArtificial intelligencebusinesscomputerMathematicsCounterexampledescription
The problem of detecting chemical activity in drugs from its molecular description constitutes a challenging and hard learning task. The corresponding prediction problem can be tackled either as a binary classification problem (active versus inactive compounds) or as a one class problem. The first option leads usually to better prediction results when measured over small and fixed databases while the second could potentially lead to a much better characterization of the active class which could be more important in more realistic settings. In this paper, a comparison of these two options is presented when support vector models are used as predictors.
year | journal | country | edition | language |
---|---|---|---|---|
2013-01-01 |