The availability of these data has boosted the development of machine learning methods for the in silico prediction of drugtarget interactions, including the AbMole Metaproterenol Sulfate seminal paper by Yamanishi et al.. In that paper the authors distinguish between prediction for ‘known’ drug compounds or targets, for which at least one interaction is present in the training set; and prediction for ‘new’ drug compounds or targets, for which no interaction in the training set is available. This results in four possible settings for predicting drug-target interaction, depending on whether the drug compounds and/or targets are known or new. The current state-of-the-art for the in silico prediction of drugtarget interaction involves methods that employ similarity measures for drug compounds and for targets in the form of kernel functions, e.g.. In this paper we generalize the applicability of the method introduced in to so-called new drug compounds, that is, drug compounds for which no interactions are known. The method, hereafter called GIP, uses known interactions of a drug for predicting novel ones by means of a regularized least square algorithm incorporating a product of kernels constructed from drug compound and target interaction profiles. We propose a simple weighted nearest neighbor algorithm, called WNN, for constructing an interaction score profile for a new drug compound using chemical and interaction information about known compounds in the dataset. The WNN method can be used as a standalone algorithm for predicting interactions for new drug compounds. It can also be directly incorporated into the GIP method for handling new drug compounds. We call the resulting combination WNN-GIP. The methods can be directly adapted to handle also unknown targets or both unknown drug compounds and targets. We test the predictive performance of WNN and WNN-GIP on four drug-target interaction networks in humans involving enzymes, ion channels, GPCRs and nuclear receptors. Results as measured by the area under the curve and area under the precision-recall curve show that the weighted nearest neighbor profile algorithm and its incorporation into the GIP method are capable to predict true interactions for new drug compounds with satisfactory accuracy. The algorithms achieve competitive or better results than the recent state-of-the-art algorithms KBMF2K and BLM-NII. KBMF2K is based on a fully probabilistic approach to model drug-target interaction, which can be applied to discover target interactions for new drug compounds. Results in indicate improved accuracy over the method introduced in. BLM-NII is an extension of the BLM method to deal with new drug compounds. In BLM-NII a drug-target interaction for a new drug compound is inferred by 
constructing an estimated interaction profile from the drug compounds in the training data. The resulting profile is then used as label information to learn an interaction model for that drug compound with the BLM method.