Dr. B. C. Roy College of Pharmacy & Allied Health Sciences, Durgapur, 713206, India.
Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
J Mol Graph Model. 2024 Jan;126:108640. doi: 10.1016/j.jmgm.2023.108640. Epub 2023 Sep 27.
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.
糖尿病（DM）是一种以高血糖为特征的慢性代谢性疾病。α-葡萄糖苷酶和α-淀粉酶由于能够将碳水化合物代谢成更简单的糖，因此被认为是治疗 2 型糖尿病的两个主要靶点。在目前的研究中，进行了化学信息学分析，以利用包含 187 种α-葡萄糖苷酶和α-淀粉酶双重抑制剂的数据集开发经过验证和可预测的模型。分别构建了具有α-葡萄糖苷酶和α-淀粉酶抑制剂活性数据集的线性、可解释和统计学稳健的 2D-QSAR 模型，旨在解释负责提高这些靶点活性的关键结构和物理化学属性。因此，模型的一些描述符指出了负责这些靶点更高活性的特定结构部分的重要性，另一方面，化合物的电离势和质量以及分子中氢键供体的数量等性质被发现对于确定数据集化合物的结合潜力至关重要。用α-葡萄糖苷酶和α-淀粉酶抑制数据点开发了具有统计学意义的 3D-QSAR 模型，以估计 3D 静电和立体场对提高这些两个靶点潜力的重要性。用同源模型的α-葡萄糖苷酶和 X 射线晶体结构的α-淀粉酶进行的分子对接实验在很大程度上支持了从化学信息学分析中得出的解释。本研究应作为设计未来α-葡萄糖苷酶和α-淀粉酶抑制剂的重要指南。此外，本研究完全使用非商业性开放访问工具进行，以确保研究的易于访问和可重复性，这可能有助于世界各地的研究人员在药物设计和发现方面开展更多工作。
