Composite machine learning strategy for natural products taxonomical classification and structural insights

Taxonomical classification of natural products (NPs) can assist in genomic and phylogenetic analysis of source organisms and facilitate streamlining of bioprospecting efforts. Here, a composite machine learning strategy marrying graph convolutional neural networks (GCNNs) and eXteme Gradient boosting (XGB) is proposed and validated for taxonomical classification of NPs in five kingdoms (Animalia, Bacteria, Chromista, Fungi, and Plantae). Our composite model, trained on 133 092 NPs from the LOTUS database, achieved five-fold cross-validated classification accuracy of 97.4%. When employed to classify out-of-sample NPs from the NP Atlas database, accuracies of 82.8% for bacteria and 86.6% for fungi were obtained. Dimensionality-reduced representations of the molecular embeddings from our composite model revealed distinct clusters of NPs that suggest a basis for enhanced classification performance. The top critical substructures from the NPs of each kingdom were also identified and compared to provide insights on structure-taxonomy relationships. Overall, this study showcases the potential of composite machine learning models for robust taxonomical classification of NPs, which can streamline discovery of NPs. A composite machine learning model combining graph and decision tree-based architectures achieved high accuracy in taxonomical classification of natural products and uncovered key structure-taxonomy relationships.