電子状態インフォマティクスによるα-glucosidase阻害剤の探索

抄録

The molecular descriptor set suggested in electronic-structure informatics (ESI) was applied to predict activity of potential candidates of α-glucosidase inhibitors. In this study, we constructed a regression model for predicting the pIC₅₀ values of the known inhibitors registered in the ChEMBL database. The obtained regression model reasonably reproduced the experimental values. After refinement of the model, we conducted in silico screening to search for active inhibitors drugs from a natural product database, called KampoDB, for traditional Japanese medicine. We have discovered some promising compounds as potential α-glucosidase inhibitors.