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Several methods have been developed for the computer-aided discovery of new protease substrates and inhibitors. In this paper, we report a novel machine learning implementation to identify high-affinity protease–inhibitor complexes. The implemented proteochemometrics algorithm consists of creating topological autocorrelation descriptors for proteases and inhibitors, and then to develop support vector machine models to relate the feature vectors to the affinity class (high or low) of hypothetical protein–inhibitor complexes based on experimental inhibition constant (Ki) values. The approach based on the autocorrelation features surpassed an atom-centred (AC) approach using AC information of inhibitors. Unique to our approach is that our final classifier could recognise 80% of inhibition-complex of new ligands to be stable or unstable using only chemical connectivity of the ligands and sequence information of the targets. Moreover, the analysis of substructure classification showed a very homogenous behaviour of the model on the whole target–ligand space. The predictor is available online at:http://www.materialsinformatics.net/autoproti.html [ABSTRACT FROM AUTHOR]