A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery
Arnaud Gaudry, Marco Pagni, Florence Mehl, Sébastien Moretti, Luis-Manuel Quirós-Guerrero, Luca Cappelletti, Adriano Rutz, Marcel Kaiser, Laurence Marcourt, Emerson Ferreira Queiroz, Jean‐Robert Ioset, Antonio Grondin, Bruno David, Jean‐Luc Wolfender, Pierre‐Marie Allard
Abstract
The Experimental Natural Products Knowledge Graph (ENPKG) framework combines a sample-centric approach with semantic enrichment to organize large heterogeneous metabolomics data sets as a knowledge graph. Harmonization of experimental data with publicly available data sets and federated queries mechanisms enable efficient information extraction and the contextualization of metabolomics studies, thereby offering exciting opportunities for drug discovery and global chemodiversity characterization.