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Updated: Sep 23, 2025

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
Published on: March 12, 2020
Data considerations for predictive modeling applied to the discovery of bioactive natural products
Hai Tao Xue1, Michael Stanley-Baker2, Adams Wai Kin Kong3
1School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
Abstract:
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges.
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