Bridging Nature, Data, and Artificial Intelligence: An Interdisciplinary Approach for Natural Products Discovery

Natural products (NPs) continue to serve as a foundational source for drug leads, owing to their unique chemical architectures, evolutionary optimisation, and structural diversity. Nevertheless, conventional natural product discovery is slow, resource-intensive, and constrained by rediscovery, dereplication inefficiencies, silent biosynthetic gene clusters (BGCs), complex structural elucidation, and insufficient integration of genomic, spectral, chemical, and biological data. The integration of multiple disciplines in NPs discovery creates extraordinary opportunities as well as significant challenges in heterogeneous data integration and interpretation. Recent advances in artificial intelligence (AI) and its tools such as machine learning (ML), deep learning (DL), graph neural networks (GNNs), chemical language models, foundation models, multimodal learning frameworks, knowledge graphs, and emerging agentic AI systems are reshaping NPs research, enabling the analysis and integration of huge and heterogeneous datasets. Their applications extend from genome and BGCs mining to metabolite annotation, spectral interpretation, dereplication, bioactivity and target prediction, virtual screening, de novo natural product-inspired design, and synthesis planning. Despite its potential, the full integration of AI in the NPs discovery pipeline is still limited owing to data scarcity, inconsistent annotations, fragmented databases, limited interoperability among community resources, and the lack of standardised metadata frameworks. This review highlights different AI technologies and their applications through the NPs pipeline with a focus on multi-omics integration and the growing ability of AI to connect biosynthetic potential with chemical structures, biological functions, and therapeutic relevance. Further current challenges and future directions toward a closed-loop AI-driven discovery pipeline that integrates computational prediction, experimental validation, and active learning are discussed.