Exploring Chemical Space

Dnr:

SNIC 2017/7-92

Type:

SNAC Small

Principal Investigator:

Astrid Henz Ryen

Affiliation:

Uppsala universitet

Start Date:

2017-05-30

End Date:

2018-06-01

Primary Classification:

10701: Annan naturvetenskap

Webpage:

Allocation

Abstract

Amongst the approximate 350.000 species of higher plants only a fractional amount has been investigated phytochemically and pharmacologically. To discover new drug leads from higher plants, the identification of a promising source plays a substantial role. In this project, we analyse the chemical space of angiosperms chemistry in order to predict the chemical and possible pharmacological potential of a plant. A combination of phylogeny, chemography and chemoinformatic tools is applied to analyse comprehensive ‘in-house’ datasets of selected NPs, against the background of a target compound with desired properties and known biological activity. ChemGPS-NP, a tool to navigate chemical property space of Natural Products, is used to analyse the distribution of selected NPs in chemical space. Based on Principal Component Analysis (PCA), it allows an efficient analysis and comparison of compounds depending on their physico-chemical properties in multiple dimensions. Further more, several statistic methods and R-Packages are used to analyse the chemical property space. Extended-connectivity fingerprints (ECFPs) and molecular framework (“Murcko fragments”) are employed as additional approach to investigate phylogenetic and chemical diversity. The analysis of selected NPs via different chemoinformatic tools can lead to the identification of several compounds with high similarity in chemical space. These compounds are likely to display the same biological activity as the target compound. Combining these results with phylogenies enables us to define the chemical potential of a plant and predict the chemical potential of related plants. This phylogenetic exploration identifies the next promising plant source in search of compounds with desired properties. In this project we show that a combination of phylogeny and in silico applications serve as a tool to predict the chemical potential of a plant. It enables a guided target selection, which eventually can be applied for a guided drug lead discovery.