Automatic discovery of transferable patterns in protein-ligand interaction networks

In recent years, the pharmaceutical industry has been confronted with rising R&D costs paired with decreasing productivity. Attrition rates for new molecules are tremendous, with a substantial number of molecules failing in an advanced stage of development. Repositioning previously approved drugs for new indications can mitigate these issues by reducing both risk and cost of development. Computational methods have been developed to allow for the prediction of drug-target interactions, but it remains difficult to branch out into new areas of application where information is scarce. Here, we present a proof-of-concept for discovering patterns in protein-ligand data using frequent itemset mining. Two key advantages of our method are the transferability of our patterns to different application domains and the facile interpretation of our recommendations. Starting from a set of known protein-ligand relationships, we identify patterns of molecular substructures and protein domains that lie at the basis of these interactions. We show that these same patterns also underpin metabolic pathways in humans. We further demonstrate how association rules mined from human protein-ligand interaction patterns can be used to predict antibiotics susceptible to bacterial resistance mechanisms.