Abstract：
Dipeptidyl peptidase-4 (DPP-4) inhibitors belong to a prominent group of pharmaceutical agents that are used in the governance of type 2 diabetes mellitus (T2DM). They exert their antidiabetic effects by inhibiting the incretin hormones like glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide which, play a pivotal role in the regulation of blood glucose homoeostasis in our body. DPP-4 inhibitors have emerged as an important class of oral antidiabetic drugs for the treatment of T2DM. Surprisingly, only a few 2D-QSAR studies have been reported on DPP-4 inhibitors. Here, fragment-based QSAR (Laplacian-modified Bayesian modelling and Recursive partitioning (RP) approaches have been utilized on a dataset of 108 DPP-4 inhibitors to achieve a deeper understanding of the association among their molecular structures. The Bayesian analysis demonstrated satisfactory ROC values for the training as well as the test sets. Meanwhile, the RP analysis resulted in decision tree 3 with 2 leaves (Tree 3: 2 leaves). This present study is an effort to get an insight into the pivotal fragments modulating DPP-4 inhibition.
摘要：
二肽基肽酶-4(DPP-4)抑制剂属于一类用于治疗2型糖尿病(T2DM)的重要药物。它们通过抑制胰高血糖素样肽-1和葡萄糖依赖性促胰岛素多肽等肠促胰岛素激素发挥其抗糖尿病作用，在调节我们体内的血糖平衡中起着关键作用。DPP-4抑制剂已成为治疗T2DM的一类重要的口服抗糖尿病药物。令人惊讶的是,只有少数2D-QSAR研究报道了DPP-4抑制剂。这里,基于片段的QSAR（拉普拉斯改进的贝叶斯建模和递归分区（RP）方法已在108个DPP-4抑制剂的数据集上使用，以更深入地了解其分子结构之间的关联。贝叶斯分析证明了训练以及测试集的令人满意的ROC值。同时,RP分析产生具有2个叶子的决策树3(树3:2个叶子)。本研究旨在深入了解调节DPP-4抑制的关键片段。
