In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.
|Title of host publication||Chemoinformatics and Advanced Machine Learning Perspectives|
|Subtitle of host publication||Complex Computational Methods and Collaborative Techniques|
|Number of pages||14|
|Publication status||Published - 2010|
All Science Journal Classification (ASJC) codes
- Agricultural and Biological Sciences(all)