There have been significant advances in various areas of the field, such as the prediction of biochemical effects of NP based on their molecular structure , in the field of genome mining for the discovery of bioactive compounds [112], the mining of mass spectrometry-based metabolomics data , and integrative approaches that combine metabolomics and genomics data . The recent development in the field of deep generative models helps researchers generate molecules with desired properties , but a model that can generalise well and can generate molecules with desirable properties requires a large amount of training data. When dealing with artificially generated structures, it is also necessary to consider their synthetic accessibility. To successfully use deep learning on published NP structures, well-curated data is essential. Published data resources are often incomplete, inaccessible, or no longer available [124] which makes available resources like the Natural Products Atlas , LOTUS [125], and COCONUT even more important.