Revolutionizing Natural Product Science with AI: Integrating Diverse Data and Knowledge Graphs

Drug discovery and development is overly complex and time-consuming process because traditional trial-and-error methods depend on inefficient data utilization approaches. Traditional methods have limited scopes and delays in identification of lead compound. Natural products are more difficult to identify, isolation, synthesis, and evaluation by traditional methods. However, recent development of scientific advances applied in field of data analysis, biologically, chemically, medicinally by using computational transformations. Artificial intelligence is the most revolutionary tool in drug discovery, development, and predicting data which includes machine learning and deep learning. Natural Products (NPs) offer invaluable value during drug discovery because they provide the basis for numerous effective compounds. The growth of NP-related databases together with AI-based analytics has enabled increased exploration of bioactive compounds. The application of AI technologies in NP research enables new drug discoveries by using predictive models together with generative AI and large-scale data synthesizing methods. This review demonstrates the latest developments in AI applications for NP drug discovery and genomics by explaining their effects on natural product biology research as well as chemical adaptations and therapeutic analysis. Through AI integration in NP research scientists will speed up drug development and detect novel bioactive molecules within an ethical regulatory framework for computational drug discovery in its current development phase. Major Findings: The review discusses the transformation redefining natural product research that artificial intelligence and its integration with knowledge graphs are driving by providing unified access to multimodal data, enhancing the predictive power of bioactivity and structural elucidation, and speeding up drug discovery pipelines. AI-based models eliminate the data fragmentation and complexity to allow the quick discovery, optimization and sustainable production of new bioactive compounds.