An In Silico Model for Interpreting Polypharmacology in Drug–Target Networks
Recent analysis on polypharmacology leads to the idea that only small fragments of drugs and targets are a key to understanding their interactions forming polypharmacology. This idea motivates us to build an in silico approach of finding significant substructure patterns from drug–target (molecular graph–amino acid sequence) pairs. This article introduces an efficient in silico method for enumerating, from given drug–target pairs, all frequent subgraph–subsequence pairs, which can then be further examined by hypothesis testing for statistical significance. Unique features of the method are its scalability, computational efficiency, and technical soundness in terms of computer science and statistics. The presented method was applied to 11,219 drug–target pairs in DrugBank to obtain significant substructure pairs, which can divide most of the original 11,219 pairs into eight highly exclusive clusters, implying that the obtained substructure pairs are indispensable components for interpreting polypharmacology.
Key wordsFrequent pattern mining Graphs Strings Likelihood-ratio test Polypharmacology Drug–target networks
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