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DOI: 10.1055/s-0034-1396322
Chemography of Natural Product Space
Publikationsverlauf
received 08. Juli 2014
revised 28. November 2014
accepted 12. Januar 2015
Publikationsdatum:
26. Februar 2015 (online)
![](https://www.thieme-connect.de/media/plantamedica/201506/lookinside/thumbnails/10-1055-s-0034-1396322_pmy0774-1.jpg)
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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