title: Chemography of natural product space.
abstract: We present the application of the generative topographic map algorithm to visualize the chemical space populated by natural products and synthetic drugs. Generative topographic maps may be used for nonlinear dimensionality reduction and probabilistic modeling. For compound mapping, we represented the molecules by two-dimensional pharmacophore features (chemically advanced template search descriptor). The results obtained suggest a close resemblance of synthetic drugs with natural products in terms of their pharmacophore features, despite pronounced differences in chemical structure. Generative topographic map-based cluster analysis revealed both known and new potential activities of natural products and drug-like compounds. We conclude that the generative topographic map method is suitable for inferring functional similarities between these two classes of compounds and predicting macromolecular targets of natural products.
authors.affiliation: Department of Chemical Systems Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Miyao
authors.affiliation: Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland.
authors.forename: Daniel
authors.initials: D
authors.lastname: Reker
authors.affiliation: Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland.
authors.forename: Petra
authors.initials: P
authors.lastname: Schneider
authors.affiliation: Department of Chemical Systems Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
authors.forename: Kimito
authors.initials: K
authors.lastname: Funatsu
authors.affiliation: Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland.
authors.forename: Gisbert
authors.initials: G
authors.lastname: Schneider
mesh_terms: D001688:Biological Products / Q000737:chemistry*; D016000:Cluster Analysis; D025341:Principal Component Analysis; D011336:Probability
chemical_list: D001688:Biological Products