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Planta Med 2015; 81(06): 429-435
DOI: 10.1055/s-0034-1396322
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Chemography of Natural Product Space

Tomoyuki Miyao   *
1   Department of Chemical Systems Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
2   Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
,
Daniel Reker   *
2   Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
,
Petra Schneider
2   Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
,
Kimito Funatsu
1   Department of Chemical Systems Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan
,
Gisbert Schneider
2   Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
› Author Affiliations
Further Information

Publication History

received 08 July 2014
revised 28 November 2014

accepted 12 January 2015

Publication Date:
26 February 2015 (online)

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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.

Key words

computational chemistry - drug discovery - generative topographic map - machine learning - polypharmacology

* These authors contributed equally to this work.


 

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