Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks.

Background: Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network.

Development and mining of a volatile organic compound database.

Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases.

KNApSAcK Metabolite Activity Database for retrieving the relationships between metabolites and biological activities.

Databases (DBs) are required by various omics fields because the volume of molecular biology data is increasing rapidly. In this study, we provide instructions for users and describe the current status of our metabolite activity DB. To facilitate a comprehensive understanding of the interactions between the metabolites of organisms and the chemical-level contribution of metabolites to human health, we constructed a metabolite activity DB known as the KNApSAcK Metabolite Activity DB.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database.

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics.

KNApSAcK-3D: a three-dimensional structure database of plant metabolites.

Studies on plant metabolites have attracted significant attention in recent years. Over the past 8 years, we have constructed a unique metabolite database, called KNApSAcK, that contains information on the relationships between metabolites and their expressing organism(s). In the present paper, we introduce KNApSAcK-3D, which contains the three-dimensional (3D) structures of all of the metabolic compounds included in the original KNApSAcK database.

Data Mining Methods for Omics and Knowledge of Crude Medicinal Plants toward Big Data Biology.

Molecular biological data has rapidly increased with the recent progress of the Omics fields, e.g., genomics, transcriptomics, proteomics and metabolomics that necessitates the development of databases and methods for efficient storage, retrieval, integration and analysis of massive data.

Efficacy prediction of jamu formulations by PLS modeling.

Indonesian herbal medicines made from mixtures of several plants are called "Jamu." The efficacy of a particular Jamu is determined by its ingredients i.e.

KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research.

A database (DB) describing the relationships between species and their metabolites would be useful for metabolomics research, because it targets systematic analysis of enormous numbers of organic compounds with known or unknown structures in metabolomics. We constructed an extensive species-metabolite DB for plants, the KNApSAcK Core DB, which contains 101,500 species-metabolite relationships encompassing 20,741 species and 50,048 metabolites. We also developed a search engine within the KNApSAcK Core DB for use in metabolomics research, making it possible to search for metabolites based on an accurate mass, molecular formula, metabolite name or mass spectra in several ionization modes.