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The effective representation of proteins is a crucial task that directlyaffects the performance of many bioinformatics problems. Related proteinsusually bind to similar ligands. Chemical characteristics of ligands are knownto capture the functional and mechanistic properties of proteins suggestingthat a ligand based approach can be utilized in protein representation. In thisstudy, we propose SMILESVec, a SMILES-based method to represent ligands and anovel method to compute similarity of proteins by describing them based ontheir ligands. The proteins are defined utilizing the word-embeddings of theSMILES strings of their ligands. The performance of the proposed proteindescription method is evaluated in protein clustering task using TransClust andMCL algorithms. Two other protein representation methods that utilize proteinsequence, BLAST and ProtVec, and two compound fingerprint based proteinrepresentation methods are compared. We showed that ligand-based proteinrepresentation, which uses only SMILES strings of the ligands that proteinsbind to, performs as well as protein-sequence based representation methods inprotein clustering. The results suggest that ligand-based protein descriptioncan be an alternative to the traditional sequence or structure basedrepresentation of proteins and this novel approach can be applied to differentbioinformatics problems such as prediction of new protein-ligand interactionsand protein function annotation.

A novel methodology on distributed representations of proteins using their interacting ligands