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Predicting drug‐target interactions based on an improved semi‐supervised learning approach
Author(s) -
Yu Weiming,
Cheng Xuan,
Li Zhibin,
Jiang Zhenran
Publication year - 2011
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.20418
Subject(s) - drug target , construct (python library) , drug discovery , drug , computer science , machine learning , artificial intelligence , chemical space , computational biology , space (punctuation) , labeled data , chemistry , bioinformatics , biology , pharmacology , biochemistry , programming language , operating system
Identifying interactions between compounds and target proteins is an important area of research in drug discovery and there is thus a strong incentive to develop computational approaches capable of detecting these potential compound‐protein interactions efficiently. In this study, two different methods were first utilized to construct chemical and genomic spaces, respectively. Then two spaces were combined into a integrate space to discover the potential compound‐target pairs in the known drug‐target interaction data by an improved semi‐supervised learning method (FLapRLS). The results demonstrated that this prediction method is effective. Drug Dev Res 72: 219–224, 2011. © 2010 Wiley‐Liss, Inc.