Artificial intelligence driven approaches in phytochemical research : trends and prospects

Abstract

Tremendous scientific advancements have been witnessed in phytochemical research in pursuit of their therapeutic and nutritional value. Leveraging artificial intelligence (AI) is essential to handle the growing omics data and for the elucidation of novel potential phytochemicals. Interestingly, AI has transformed phytochemical research by enabling the efficient analysis of high-dimensional ‘omics’ data and facilitating the discovery of novel metabolites, structural elucidation, and metabolite profiling in plants. Taking together, this review highlights the implementation and significance of AI in various aspects of phytochemical research including analytical techniques, structural elucidation of phytochemicals, plant metabolomics, and genomics. The review also provides an outlook of prominent computational tools in phytochemical research including CASE followed by the present status and challenges of implementing AI in phytochemical research. We also propose the integration of more AI-driven analytical approaches in phytochemical research for the discovery of metabolites and to explore their applications in medicine and agriculture.