Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular patterns of these privileged structures for combinatorial design or target selectivity.

Automated summary

Natural products are considered privileged structures to interact with protein drug targets, sparking interest in developing NP-inspired medicines. The advancement of artificial intelligence has democratized the field of natural product drug discovery, with the introduction of natural language processing and machine learning algorithms enhancing molecular design and target selectivity.