Abstract：
The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha\'s analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.
摘要：
对可能具有DPP-4抑制活性的已知药物的可能先导化合物的预测意味着在药物开发中在寻找用于治疗2型糖尿病(T2DM)的替代药物的时间和成本方面的优势。抑制二肽基肽酶-4（DPP-4）是开发针对这种情况的潜在药物的最多探索策略之一。构建了具有已知的针对DPP-4的实验抑制活性的分子的不同数据集，并用于使用不同的机器学习算法开发预测模型。模型M36是最有前途的一个基于内部和外部性能显示值Q2CV=0.813和Q2EXT=0.803。进行了适用性领域评估和Tropsha分析以验证M36，表明其在预测已建立领域内有机分子的pIC50值方面的稳健性和准确性。配体的物理化学性质，包括电负性,极化率，范德华体积与预测抑制过程有关。然后将该模型用于潜在的DPP4抑制剂的虚拟筛选，发现来自DrugBank的448种化合物和来自DiaNat的9种化合物具有潜在的抑制活性。使用分子对接和分子动力学模拟来深入了解配体-蛋白质相互作用。从筛选和有利的分子动力学结果来看，几种化合物，包括Skimmin（pIC50=3.54，结合能=-8.86kcal/mol），岩白菜碱（pIC50=2.69，结合能=-13.90kcal/mol），和DB07272(pIC50=3.97,结合能=-25.28kcal/mol)似乎是在T2DM治疗中测试和优化的有希望的命中。该结果意味着该药物应用的成本和时间的重要降低，因为有关其代谢的所有信息已经可用。
