The Small Molecule Accurate Recognition Technology (SMART 2.0; , coordinated by Prof. William Gerwick at the University of California San Diego), has a different but complementary approach. It is an infrastructure that uses machine learning (ML) with an artificial intelligence (AI) algorithm based on convolutional neural networks (CNN) to allocate the experimental HSQC data in a multidimensional space (the moliverse). The spacial position of the queried data in the moliverse suggests, by approximation, the identity of the compounds occuring in the sample, promising to accelerate the rate of discovery of new natural products. The idea is based on a concept similar to that used in facial recognition, where the HSQC data replace the photos. The moliverse is composed of over 100,000 known compounds with both experimental and simulated NMR data. In the SMART web portal the experiments can be performed and the results are shown in the form of SMILES structures (representation of chemical structures using normal characters; Simplified Molecular-Input Line-Entry System) together with the similarity parameters (cosine), molecular mass and links for interaction with other databases, namely GNPS, MIBiG 12 e NPAtlas. The application of SMART-NMR for mixtures is limited since it depends on the peak profile in the HSQC spectra. It can alocate a pure spectra within the chemical space of its molecular structure quite successfully to aid the structure elucidation of purified compounds. In addition, it has already been used for mixtures (fractions of a chromatographic separation) and the results were helpful in prioritizing samples for structure determination. Together with LC-MS/MS and bioactivity data, SMART-NMR enables the early detection of potentially active compounds. This is definetely a tool worth being utilized, especially for those struggling with the assignment of peaks to new structures since it suggests similar structures from the HSQC spectra. A demonstration was done using data of azythromycin, paclitaxel, and ginsenoside RG1. These three datasets were obtained from the fiblib of Agilent/Varian software. Figure shows the results produced by SMART-NMR for azythromycin. The analyses using SMART-NMR for the HSQC data of azythromycin, paclitaxel, and ginsenoside RG1 are presented in full in SI (Figures SI.3