title: Predicting drug targets based on protein domains.
abstract: The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.
authors.affiliation: Department of Mathematics, Shanghai University, Shanghai 200444, China.
authors.forename: Yin-Ying
authors.initials: YY
authors.lastname: Wang
authors.forename: Jose C
authors.initials: JC
authors.lastname: Nacher
authors.forename: Xing-Ming
authors.initials: XM
authors.lastname: Zhao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D058990:Molecular Targeted Therapy / Q000379:methods*; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins