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

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Abstract

This report describes the first application of the novel NMR-based machine learning tool ‘Small Molecule Accurate Recogni-tion 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 identification of swinholide A, sam-holides A-I and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid be-tween swinholide A and luminaolide B by 1D/2D NMR and LCMS2 analysis. A second example applies SMART 2.0 to the char-acterization 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.