Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

Shuangjia Zheng; Tao Zeng; Chengtao Li; Binghong Chen; Connor W. Coley; Yuedong Yang; Ruibo Wu

doi:10.1038/s41467-022-30970-9

Nature Communications (Jun 2022)

Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

Shuangjia Zheng,
Tao Zeng,
Chengtao Li,
Binghong Chen,
Connor W. Coley,
Yuedong Yang,
Ruibo Wu

Affiliations

Shuangjia Zheng: School of Pharmaceutical Sciences, Sun Yat-sen University
Tao Zeng: School of Pharmaceutical Sciences, Sun Yat-sen University
Chengtao Li: Galixir
Binghong Chen: College of Computing, Georgia Institute of Technology
Connor W. Coley: Department of Chemical Engineering, Massachusetts Institute of Technology
Yuedong Yang: School of Computer Science and Engineering, Sun Yat-sen University
Ruibo Wu: School of Pharmaceutical Sciences, Sun Yat-sen University

DOI: https://doi.org/10.1038/s41467-022-30970-9
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 9

Abstract

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The complete biosynthetic pathway from most natural products (NPs) are unknown. Here, the authors report BioNavi-NP, a computational toolkit for bio-retrosynthetic pathway elucidation or reconstruction for both NPs and NP-like compounds.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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