Target recognition and network pharmacology for revealing anti-diabetes mechanisms of natural product

Diabetes is a metabolic disease characterized by persistent hyperglycemia, which results in serious complications such as cardiovascular disorder, neuropathy, renal failure, and retinopathy. Various anti diabetic drugs have been developed, however, their therapeutic effect is far from satisfactory. Therefore, increasing attention has been paid to natural products, whose multi-targets characteristics might contribute to treating polygenetic disease like diabetes. Indeed, valuable implications for anti-diabetic drug discovery can be obtained through exploring the molecular mechanism of these natural products, however, it could be a formidable task with traditional experimental approaches. As such, computational approaches are applied in predicting the compound-target interactions, including molecular docking, molecular dynamics (MD) simulations, and other computer aided drug design (CADD) methods. Furthermore, network pharmacology strategy is introduced for exploring natural products with anti-diabetic activities, including the general workflow of network pharmacology, its application in anti-diabetic traditional Chinese medicine formula research, and the application of natural products in diabetic complications. In particular, a promising prospective insight of combining computational methods and network pharmacology in anti-diabetic drug discovery is discussed. In summary, the combination of computational approaches and network pharmacology has already produced fruitful natural products with anti-diabetic effect, and will hopefully initiate novel anti-diabetic drug development with advanced therapeutic effect and less side effect. (C) 2020 Elsevier B.V. All rights reserved.