In this study, two probabilistic machine-learning algorithms were compared for in silico target prediction of bioactive molecules, namely the well-established Laplacian-modified Naïve Bayes classifier (NB) and the more recently introduced (to Cheminformatics) Parzen-Rosenblatt Window. Both classifiers were trained in conjunction with circular fingerprints on a large data set of bioactive compounds extracted from ChEMBL, covering 894 human protein targets with more than 155,000 ligand-protein pairs. This data set is also provided as a benchmark data set for future target prediction methods due to its size as well as the number of bioactivity classes it contains. In addition to evaluating the methods, different performance measures were explored. This is not as straightforward as in binary classification settings, due to the number of classes, the possibility of multiple class memberships, and the need to translate model scores into "yes/no" predictions for assessing model performance. Both algorithms achieved a recall of correct targets that exceeds 80% in the top 1% of predictions. Performance depends significantly on the underlying diversity and size of a given class of bioactive compounds, with small classes and low structural similarity affecting both algorithms to different degrees. When tested on an external test set extracted from WOMBAT covering more than 500 targets by excluding all compounds with Tanimoto similarity above 0.8 to compounds from the ChEMBL data set, the current methodologies achieved a recall of 63.3% and 66.6% among the top 1% for Naïve Bayes and Parzen-Rosenblatt Window, respectively. While those numbers seem to indicate lower performance, they are also more realistic for settings where protein targets need to be established for novel chemical substances.

Similarity searches using combinations of seven different similarity coefficients and six different representations have been carried out on the Dictionary of Natural Products database. The objective was to discover if any special methods of searching apply to this database, which is very different in nature from the many synthetic databases that have been the subject of previous studies of similarity searching. Search effectiveness was assessed by a recall analysis of the search outputs from sets of pharmacologically active target structures. The different target sets produce exceptional but contradictory results for the Russell-Rao and Forbes coefficients, which have been shown to be due to a dependence on molecular size; these are the coefficients of choice in the case of large and small structures, respectively. Rankings from these results have been combined using a data fusion scheme and some small gains in performance were normally obtained by using substructural fingerprints and molecular holograms in combination with the Squared Euclidean or Tanimoto coefficients.

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Both metabolism and transport are key elements defining the bioavailability and biological activity of molecules, i.e. their adverse and therapeutic effects. Structured and high quality experimental data stored in a suitable container, such as a relational database, facilitates easy computational processing and thus allows for high quality information/knowledge to be efficiently inferred by computational analyses. Our aim was to create a freely accessible database that would provide easy access to data describing interactions between proteins involved in transport and xenobiotic metabolism and their small molecule substrates and modulators. We present Metrabase, an integrated cheminformatics and bioinformatics resource containing curated data related to human transport and metabolism of chemical compounds. Its primary content includes over 11,500 interaction records involving nearly 3,500 small molecule substrates and modulators of transport proteins and, currently to a much smaller extent, cytochrome P450 enzymes. Data was manually extracted from the published literature and supplemented with data integrated from other available resources. Metrabase version 1.0 is freely available under a CC BY-SA 4.0 license at http://www-metrabase.ch.cam.ac.uk.Graphical AbstractElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0083-5) contains supplementary material, which is available to authorized users.