Is scaffold hopping possible in machine learning using the electronic-structure-informatics (ESI) descriptor set? an application to natural-product-based drug discovery of α-glucosidase inhibitors.

内容摘要

The electronic-structure informatics (ESI) descriptor set was applied to discover novel α-glucosidase inhibitors from a natural product (NP) database. The in silico screening was carried out through regression modelling for inhibitory activity (pIC50) using XGBoost with the ESI descriptor set. The optimized model achieved a test R2 of 0.85, demonstrating its high predictive accuracy. To explore potent NPs for α-glucosidase inhibition, in silico screening of 2623 NPs was performed. Already known NP-α-glucosidase inhibitors such as theasinensin A, chebulagic acid, and casuarictin were "re-identified" through the screening. It also revealed structurally novel NP compounds with moderate inhibitory activity and new scaffolds different from those of the known inhibitors. A series of docking simulations on the newly discovered compounds revealed that their binding scores are higher than a marketed drug, acarbose. These results demonstrate the applicability and uniqueness of the ESI descriptor set in "scaffold hopping" using NP databases.Scientific contributionThis study shows that the electronic-structure informatics (ESI) descriptor set supports effective scaffold hopping for discovering α-glucosidase inhibitors from natural product (NP) libraries beyond conventional structure-based searches. By combining quantum-chemistry-derived ESI descriptors with machine learning, we identify structurally novel NP inhibitor candidates, which exhibit competitive predicted activity despite low similarity to known chemotypes. This work demonstrates the value of electronic-structure information in in silico screening for identifying chemically diverse candidates.