A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products

This
report describes the first application of the novel NMR-based
machine learning tool “Small Molecule Accurate Recognition
Technology” (SMART 2.0) for mixture analysis and subsequent
accelerated discovery and characterization of new natural products.
The concept was applied to the extract of a filamentous marine cyanobacterium
known to be a prolific producer of cytotoxic natural products. This
environmental Symploca extract was roughly fractionated,
and then prioritized and guided by cancer cell cytotoxicity, NMR-based
SMART 2.0, and MS2-based molecular networking. This led
to the isolation and rapid identification of a new chimeric swinholide-like
macrolide, symplocolide A, as well as the annotation of swinholide
A, samholides A–I, and several new derivatives. The planar
structure of symplocolide A was confirmed to be a structural hybrid
between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization
of structurally novel cyclic peptides, and compares this approach
to the recently appearing “atomic sort” method. This
study exemplifies the revolutionary potential of combined traditional
and deep learning-assisted analytical approaches to overcome longstanding
challenges in natural products drug discovery.