biostudies-literatureKim HWNetherlands eScience CenterGordon and Betty Moore FoundationNational Research Foundation of KoreaNational Institute of General Medical SciencesNIGMS NIH HHS2795-2807https://www.ebi.ac.uk/biostudies/studies/S-EPMC8631337biostudies-literatureUnknown84(11)Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.Journal of natural productsNPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.PMC8631337ASDI.2017.030R01 GM107550NRF-2020R1C1C1004046GBMF7622Wang MNothias LFCottrell GWKim HWLeber CAReher Rvan der Hooft JJJDorrestein PCGerwick WHKang KBfalseNPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products.Computational approaches such as genome and metabolome mining are becoming essential to natural products (NPs) research. Consequently, a need exists for an automated structure-type classification system to handle the massive amounts of data appearing for NP structures. An ideal semantic ontology for the classification of NPs should go beyond the simple presence/absence of chemical substructures, but also include the taxonomy of the producing organism, the nature of the biosynthetic pathway, and/or their biological properties. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs, and especially so when analyzing large numbers of NPs. Here, we introduce NPClassifier, a deep-learning tool for the automated structural classification of NPs from their counted Morgan fingerprints. NPClassifier is expected to accelerate and enhance NP discovery by linking NP structures to their underlying properties.2021-01-01T00:00:00Z2021 Nov2025-04-05T10:28:52.596Z2025-04-05T10:28:52.596ZS-EPMC86313373466251510.1021/acs.jnatprod.1c00399