Computational insights into drug discovery from Vernonia amygdalina: a narrative review

Abstract

This narrative review discusses how bioinformatics, chemo-informatics, genomics, metabolomics, and computational biology can help us learn more about and use the bioactive chemicals found in Vernonia amygdalina, known as "bitter leaf." By combining computational tools with conventional pharmacognostic procedures, researchers can efficiently explore the potential of this medicinal plant for drug development and discovery. Throughout our discussion, we underscored the symbiotic relationship between computational and experimental approaches, highlighting their collaborative efforts in unveiling the pharmacological properties and therapeutic prospects of V. amygdalina. Bioinformatics helps to process complicated biological data whereas computational biology models molecular interactions and predicts possible therapeutic targets. Genomics offers insights into the genetic basis of therapeutic characteristics whereas chemo-informatics assists in creating and optimizing bioactive molecules. Insight into a plant's chemical makeup and regulation of metabolic processes entails metabolomics. These fields when integrated can provide a comprehensive understanding of V. amygdalina’s prospect in drug discovery. It will open the door to the development of new treatment approaches. Thus, we suggest that this multidisciplinary strategy increases the novel explorations of natural resources in fighting different diseases and further speeds up drug discovery procedures.