This is the final version. It was first published by RSC at http://pubs.rsc.org/en/content/articlelanding/2015/md/c4md00216d#!divAbstract. ; Proteochemometric (PCM) modelling is a computational method to model the bioactivity of multiple ligands against multiple related protein targets simultaneously. Hence it has been found to be particularly useful when exploring the selectivity and promiscuity of ligands on different proteins. In this review, we will firstly provide a brief introduction to the main concepts of PCM for readers new to the field. The next part focuses on recent technical advances, including the application of support vector machines (SVMs) using different kernel functions, random forests, Gaussian processes and collaborative filtering. The subsequent section will then describe some novel practical applications of PCM in the medicinal chemistry field, including studies on GPCRs, kinases, viral proteins (e.g. from HIV) and epigenetic targets such as histone deacetylases. Finally, we will conclude by summarizing novel developments in PCM, which we expect to gain further importance in the future. These developments include adding three-dimensional protein target information, application of PCM to the prediction of binding energies, and application of the concept in the fields of pharmacogenomics and toxicogenomics. This review is an update to a related publication in 2011 and it mainly focuses on developments in the field since then. ; ICC thanks the Pasteur-Paris International PhD Program and Institut Pasteur Paris for funding. QUA thanks the Islamic Development Bank and Cambridge Commonwealth Trust for funding. VS thanks the Finnish National Doctoral Program in Informational and Structural Biology for organizing graduate studies and Helsinki University Research Foundation for funding. API and EBL thank the Dutch Research Council (NWO) for financial support (NWO-TOP #714.011.001). OML is grateful to CONACyT (No. 217442/312933) and Cambridge Overseas Trust for funding. TM thanks the Institut Pasteur Paris and CNRS for funding. GJPvW thanks EMBL (EIPOD) and Marie Curie (COFUND) for funding. AB thanks Unilever and the European Research Commission (Starting Grant ERC-2013-StG 336159 MIXTURE) for funding.