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Background Proteochemometrics (PCM) is a predictive bioactivity modelling method which simultaneously models the bioactivity of multiple ligands against multiple targets. PCM permits exploration of the selectivity and promiscuity of ligands on biomolecular systems of different complexity. This includes proteins and even cell-line models [1,2]. The suitability of PCM to predict compound polypharmacology has been validated both retrospectively and in prospective experimental validation [1,2]. In practice, each ligand-target interaction is encoded by the concatenation of ligand and target descriptor vectors used to train a single machine learning model. The inclusion of both chemical and target information enables the extraand interpolation on the chemical and on the biological space. Therefore, PCM permits to predict compound bioactivities on targets not present in the training phase [3].

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling