A sample-centric and knowledge-driven computational framework for natural products drug discovery

By Gaudry A, Pagni M, Mehl F, Moretti S, Quiros-Guerrero L-M, Cappelletti L, Rutz A, Kaiser M, Marcourt L, Ferreira Queiroz E, Ioset J-R, Grondin A, David B, Wolfender J-L, Allard P-M. ACS Central Science 2024. doi: 10.1021/acscentsci.3c00800

Summary: In this manuscript, the authors describe a novel approach based on semantic web technology to organize, standardize, annotate, and compare complex metabolomics data associated with natural product drug discovery programs. They explored a collection of 1,600 plant extracts that was screened against trypanosomatids and were able to rapidly annotate known and unknown anti-T. cruzi compounds in active extracts and to identify an active analog of reported anti-L. donovani compounds. The framework developed offers the possibility of exploring natural product metabolomics data in the context of drug discovery and beyond. This work was done in collaboration with the University of Geneva, Institute de Recherche de Pierre Fabre, and the Swiss Tropical and Public Health Institute.

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