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Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor