Predicting drug‐target interactions based on an improved semi‐supervised learning approach

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.

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