Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.

当前药物-靶点网络（Drug-Target Network）的一项核心特征是，药物通常可结合多个靶点，这一特性被称为多药理学（polypharmacology）或药物混杂性（drug promiscuity）。现有研究文献表明，药物与靶点双方的较小片段，对于多药理学的形成至关重要。我们提出假说：多药理学背后的作用机制，蕴含于药物-靶点相互作用（Drug-Target Interaction）的分子图与氨基酸序列的配对片段之中。我们开发了一种快速且可扩展的算法，用于从药物-靶点相互作用数据集中挖掘显著共现的子图-子序列对。本方法的一项显著优势在于，能够捕捉子图-子序列的显著配对模式，而截至目前的相关研究仅考虑了药物或靶点单方的模式。显著的子结构对可将药物-靶点相互作用划分为多个簇，覆盖了约75%的获批药物相关相互作用。这些簇彼此间具有高度排他性，且统计学意义显著，这在逻辑上表明每个簇对应一类独特的多药理学模式。仅使用药物或靶点单方信息无法轻易得到这些排他性簇，而通过突出药物-靶点相互作用中的显著子结构对，则可自然地挖掘出这些簇。上述结果证实了我们的方法在解析药物-靶点网络中的多药理学机制方面的有效性。