Abstract
Natural products provide valuable starting points for new drugs with unique chemical structures. Here, we retrieve and join the LOTUS natural product database and ChEMBL interaction database to explore the relations and rhythm between chemical features of natural products and biotarget spaces. Our analysis revealed relations between the biogenic pathways of natural products and species taxonomy. Nitrogen-containing natural products were more likely to achieve high activity and have a higher potential to become candidate compounds. An apparent trend existed in the target space of natural products originating from different biological sources. Highly active alkaloids were more related to targets of neurodegenerative or neural diseases. Oligopeptides and polyketides were mainly associated with protein phosphorylation and HDAC receptors. Fatty acids readily intervened in various physiological processes involving prostanoids and leukotrienes. We also used FusionDTA, a deep learning model, to predict the affinity between all LOTUS natural products and 622 therapeutic drug targets, exploring the potential target space for natural products. Our data exploration provided a global perspective on the gaps in the chemobiological space of natural compounds through systematic analysis and prediction of their target space, which can be used for new drug design or natural drug repurposing.