title: Prediction of bioactive compound pathways using chemical interaction and structural information.
abstract: The functional screening of compounds is an important topic in chemistry and biomedicine that can uncover the essential properties of compounds and provide information concerning their correct use. In this study, we investigated the bioactive compounds reported in Selleckchem, which were assigned to 22 pathways. A computational method was proposed to identify the pathways of the bioactive compounds. Unlike most existing methods that only consider compound structural information, the proposed method adopted both the structural and interaction information from the compounds. The total accuracy achieved by our method was 61.79% based on jackknife analysis of a dataset of 1,832 bioactive compounds. Its performance was quite good compared with that of other machine learning algorithms (with total accuracies less than 46%). Finally, some of the false positives obtained by the method were analyzed to investigate the likelihood of compounds being annotated to new pathways.
authors.forename: Shiwen
authors.initials: S
authors.lastname: Cheng
authors.forename: Changming
authors.initials: C
authors.lastname: Zhu
authors.forename: Chen
authors.initials: C
authors.lastname: Chu
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: College of Life Science, Shanghai University, Shanghai 200444, People's Republic of China. purk1983@163.com.
authors.forename: Liu Cun
authors.initials: LC
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D062126:Databases, Chemical; D057166:High-Throughput Screening Assays; D000069550:Machine Learning; D056004:Molecular Dynamics Simulation; D015394:Molecular Structure; D054852:Small Molecule Libraries / Q000737:chemistry*
chemical_list: D054852:Small Molecule Libraries