Abstract Motivation: Construction of synthetic metabolic pathways promises sustainable production of diverse chemicals and materials. To design synthetic metabolic pathways of high value, computational methods are needed to expand present knowledge by mining comprehensive chemical and enzymatic information databases. Several computational methods have been already reported for the metabolic pathway design, but until now computation complexity has limited the diversity of chemical and enzymatic data used. Results: We introduce a computational platform, M-path, to explore synthetic metabolic pathways including putative enzymatic reactions and compounds. M-path is an iterative random algorithm, which makes efficient use of chemical and enzymatic databases to find potential synthetic metabolic pathways. M-path can readily control the search space and perform well compared with exhaustively enumerating possible pathways. A web-based pathway viewer is also developed to check extensive metabolic pathways with evaluation scores on the basis of chemical similarities. We further produce extensive synthetic metabolic pathways for a comprehensive set of alpha amino acids. The scalable nature of M-path enables us to calculate potential metabolic pathways for any given chemicals. Availability and implementation: The web tool and viewer are available for free at http://bp.scitec.kobe-u.ac.jp/m-path/aa/. Contact: [email protected] and [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently. RESULTS: In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug-target interaction network topology. We then develop new statistical methods to predict unknown drug-target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug-target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call 'pharmacological space'. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug-target interaction networks. Our comprehensively predicted drug-target interaction networks enable us to suggest many potential drug-target interactions and to increase research productivity toward genomic drug discovery. AVAILABILITY: Softwares are available upon request. SUPPLEMENTARY INFORMATION: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.