title: Prediction of compound-target interactions of natural products using large-scale drug and protein information.
abstract: BACKGROUND
  Verifying the proteins that are targeted by compounds of natural herbs will be helpful to select natural herb-based drug candidates. However, this entails a great deal of effort to clarify the interaction throughout in vitro or in vivo experiments. In this light, in silico prediction of the interactions between compounds and target proteins can help ease the efforts.
  
  
  RESULTS
  In this study, we performed in silico predictions of herbal compound target identification. First, data related to compounds, target proteins, and interactions between them are taken from the DrugBank database. Then we characterized six classes of compound-target interaction in humans including G-protein-coupled receptors (GPCRs), ion channel, enzymes, receptors, transporters, and other proteins. Also, classification-prediction models that predict the interactions between compounds and target proteins through a machine learning method were constructed using these matrices. As a result, AUC values of six classes are 0.94, 0.93, 0.90, 0.89, 0.91, and 0.76 respectively. Finally, the interactions of compounds from natural products were predicted using the constructed classification models. Furthermore, from our predicted results, we confirmed that several important disease related proteins were predicted as targets of natural herbal compounds.
  
  
  CONCLUSIONS
  We constructed classification-prediction models that predict the interactions between compounds and target proteins. The constructed models showed good prediction performances, and numbers of potential natural compounds target proteins were predicted from our results.
authors.affiliation: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.
authors.forename: Jongsoo
authors.initials: J
authors.lastname: Keum
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea.
authors.forename: Sunyong
authors.initials: S
authors.lastname: Yoo
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea.
authors.forename: Doheon
authors.initials: D
authors.lastname: Lee
authors.affiliation: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea. hjnam@gist.ac.kr.
authors.forename: Hojung
authors.initials: H
authors.lastname: Nam
mesh_terms: D001688:Biological Products / Q000032:analysis*; D003198:Computer Simulation; D055808:Drug Discovery; D008956:Models, Chemical; D010946:Plants, Medicinal / Q000737:chemistry*; D011485:Protein Binding; D060388:Support Vector Machine
chemical_list: D001688:Biological Products