Data considerations for predictive modeling applied to the discovery of bioactive natural products

Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges.

Automated summary

Natural products are a valuable resource for drug development, but analyzing their complex data is a challenge. Artificial intelligence techniques can help overcome this limitation. However, further work is needed in knowledge and resource development, as well as modeling considerations, limitations, and challenges.