The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, is developed to predict the biosynthetic pathways for both NPs and NP-like compounds. First, a single-step bio-retrosynthesis prediction model is trained using both general organic and biosynthetic reactions through end-to-end transformer neural networks. Based on this model, plausible biosynthetic pathways can be efficiently sampled through an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP can identify biosynthetic pathways for 90.2% of 368 test compounds and recover the reported building blocks as in the test set for 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. The model is further shown to identify biologically plausible pathways for complex NPs collected from the recent literature. The toolkit as well as the curated datasets and learned models are freely available to facilitate the elucidation and reconstruction of the biosynthetic pathways for NPs.

Natural products (NPs) exhibit diverse chemical structures and biological activities that make them valuable sources for drug discovery. With advancements in computational technology, computation-enabled natural drug discovery is gaining increasing significance, with NP databases playing a pivotal role. In light of this, we first summarize the key features of NP databases, including structural data, property annotations, biological sources, biosynthetic pathways, and web interfaces. Subsequently, the wide applications of these databases in drug discovery, such as virtual screening, knowledge graph construction, and molecular generation, are reviewed. We further discuss the puzzle of database development, focusing on data quality and updating. Finally, we emphasize the pivotal role of team collaboration and toolkit innovation in harnessing the immense potential of NP-related databases to accelerate bioactivity mining, structure modification, and manufacturing. This review aims to elucidate the key features and applications of NP databases, with the goal of aiding researchers in developing and maintaining high-quality NP databases for drug discovery.