DINIES: drug–target interaction network inference engine based on supervised analysis

DINIES (drug–target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug–target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any ‘profiles’ or precalculated similarity matrices (or ‘kernels’) of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.

[1]  Jean-Loup Faulon,et al.  Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor , 2008 .

[2]  Yasubumi Sakakibara,et al.  Statistical prediction of protein-chemical interactions based on chemical structure and mass spectrometry data , 2007, Bioinform..

[3]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[4]  Tatsuya Akutsu,et al.  Protein homology detection using string alignment kernels , 2004, Bioinform..

[5]  M. Kanehisa,et al.  Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. , 2003, Journal of the American Chemical Society.

[6]  Yoshihiro Yamanishi,et al.  Drug target prediction using adverse event report systems: a pharmacogenomic approach , 2012, Bioinform..

[7]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[8]  YamanishiYoshihiro,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008 .

[9]  L. Holm,et al.  The Pfam protein families database , 2005, Nucleic Acids Res..

[10]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[11]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[12]  Tatsuya Akutsu,et al.  Graph Kernels for Molecular Structure-Activity Relationship Analysis with Support Vector Machines , 2005, J. Chem. Inf. Model..

[13]  E. Birney,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[14]  B. Roth,et al.  The Multiplicity of Serotonin Receptors: Uselessly Diverse Molecules or an Embarrassment of Riches? , 2000 .

[15]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[16]  Yoshihiro Yamanishi,et al.  Supervised prediction of drug–target interactions using bipartite local models , 2009, Bioinform..

[17]  Yang Song,et al.  Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery , 2011, Nucleic Acids Res..

[18]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[19]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[20]  Gong-Hua Li,et al.  CDRUG: a web server for predicting anticancer activity of chemical compounds , 2012, Bioinform..

[21]  Yoshihiro Yamanishi,et al.  Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework , 2010, Bioinform..

[22]  Kris Popendorf,et al.  COPICAT: a software system for predicting interactions between proteins and chemical compounds , 2012, Bioinform..

[23]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[24]  Damian Szklarczyk,et al.  STITCH 2: an interaction network database for small molecules and proteins , 2009, Nucleic Acids Res..

[25]  G. Hong,et al.  Nucleic Acids Research , 2015, Nucleic Acids Research.

[26]  Roded Sharan,et al.  An Algorithmic Framework for Predicting Side-Effects of Drugs , 2010, RECOMB.

[27]  Jean-Philippe Vert,et al.  Protein-ligand interaction prediction: an improved chemogenomics approach , 2008, Bioinform..

[28]  Robert B. Russell,et al.  SuperTarget and Matador: resources for exploring drug-target relationships , 2007, Nucleic Acids Res..