NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

Hyun Woo Kim, Mingxun Wang, Christopher A. Leber, Louis Félix Nothias, Raphael Reher, Kyo Bin Kang, Justin J.J. Van Der Hooft, Pieter C. Dorrestein, William H. Gerwick*, Garrison W. Cottrell

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2795-2807
JournalJournal of Natural Products
Volume84
Issue number11
Early online date18 Oct 2021
DOIs
Publication statusPublished - 2021

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