BACKGROUND: Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab. Furthermore, the considered problems are often complex, such that it is not clear which molecular descriptors (features) may be suitable to establish a strong correlation with the target property. In many applications all available descriptors are used. This can lead to difficult machine learning problems, when thousands of descriptors are considered and only few (e.g. below hundred) molecules are available for training. RESULTS: The CoEPrA contest provides four data sets, which are typical for biological regression problems (few molecules in the training data set and thousands of descriptors). We applied the same two-step training procedure for all four regression tasks. In the first stage, we used optimized L1 regularization to select the most relevant features. Thus, the initial set of more than 6,000 features was reduced to about 50. In the second stage, we used only the selected features from the preceding stage applying a milder L2 regularization, which generally yielded further improvement of prediction performance. Our linear model employed a soft loss function which minimizes the influence of outliers. CONCLUSIONS: The proposed two-step method showed good results on all four CoEPrA regression tasks. Thus, it may be useful for many other biological prediction problems where for training only a small number of molecules are available, which are described by thousands of descriptors.

MOTIVATION: 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. AVAILABILITY: 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.