M
Masaaki Kotera
Researcher at University of Tokyo
Publications - 63
Citations - 2231
Masaaki Kotera is an academic researcher from University of Tokyo. The author has contributed to research in topics: KEGG & Enzyme Commission number. The author has an hindex of 22, co-authored 63 publications receiving 1906 citations. Previous affiliations of Masaaki Kotera include Trinity College, Dublin & Kyushu University.
Papers
More filters
Journal ArticleDOI
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
TL;DR: It is shown that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity, and a new method to predict unknown drug– target interactions from chemical, genomic and pharmacological data on a large scale is developed.
Journal ArticleDOI
PathPred: an enzyme-catalyzed metabolic pathway prediction server
Yuki Moriya,Daichi Shigemizu,Masahiro Hattori,Toshiaki Tokimatsu,Masaaki Kotera,Susumu Goto,Minoru Kanehisa +6 more
TL;DR: This server presents PathPred, a web-based server to predict plausible pathways of muti-step reactions starting from a query compound, based on the local RDM pattern match and the global chemical structure alignment against the reactant pair library.
Journal ArticleDOI
Computational Assignment of the EC Numbers for Genomic-Scale Analysis of Enzymatic Reactions
TL;DR: A computerized method to automatically assign the EC numbers up to the sub-subclasses, i.e., without the fourth serial number for substrate specificity, given pairs of substrates and products is reported.
Journal ArticleDOI
Drug side-effect prediction based on the integration of chemical and biological spaces.
TL;DR: A new method to predict potential side-effect profiles of drug candidate molecules based on their chemical structures and target protein information on a large scale and several extensions of kernel regression model for multiple responses to deal with heterogeneous data sources are developed.
Journal ArticleDOI
Drug target prediction using adverse event report systems
TL;DR: A comprehensive prediction for potential off-targets of 1874 drugs with known targets and potential target profiles of 2519 drugs without known targets is made, which suggests many potential drug–target interactions that were not predicted by previous chemogenomic or pharmacogenomic approaches.