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Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Capecchi Alice, Reymond Jean-Louis,
Project Chemical Space Design of Small Molecules and Peptides
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Original article (peer-reviewed)

Journal Biomolecules
Volume (Issue) 10(10)
Page(s) 1385 - 1385
Title of proceedings Biomolecules
DOI 10.3390/biom10101385

Open Access

Type of Open Access Publisher (Gold Open Access)


Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.