Exploring classification strategies with the CoEPrA 2006 contest.
BACKGROUND
In silico methods to classify compounds as potential drugs that bind to a specific target become increasingly important for drug design. To build classification devices training sets of drugs with known activities are needed. For many such classification problems, not only qualitative but also quantitative information of a specific property (e.g. binding affinity) is available. The latter can be used to build a regression scheme to predict this property for new compounds. Predicting a compound property explicitly is generally more difficult than classifying that the property lies below or above a given threshold value. Hence, an indirect classification that is based on regression may lead to poorer results than a direct classification scheme. In fact, initially researchers are only interested to classify compounds as potential drugs. The activities of these compounds are subsequently measured in wet lab.


RESULTS
We propose a novel approach that uses available quantitative information directly for classification rather than first using a regression scheme. It uses a new type of loss function called weighted biased regression. Application of this method to four widely studied datasets of the CoEPrA contest (Comparative Evaluation of Prediction Algorithms, http://coepra.org) shows that it can outperform simple classification methods that do not make use of this additional quantitative information.


BACKGROUND
A stand alone application is available at the webpage http://agknapp.chemie.fu-berlin.de/agknapp/index.php?menu=software&page=PeptideClassifier that can be used to build a model for a peptide training set to be submitted.
Demir-Kavuk
Ozgur
O
Institute of Chemistry and Biochemistry, Free University of Berlin, Fabeckstrasse 36A, 14195 Berlin, Germany.
Riedesel
Henning
H
Knapp
Ernst-Walter
EW
D000465:Algorithms; D001665:Binding Sites; D016208:Databases, Factual; D015195:Drug Design; D008024:Ligands; D010455:Peptides / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
D008024:Ligands; D010455:Peptides