新型DPP-IV抑制剂的虚拟筛选与动力学验证
Virtual Screening and Kinetic Validation of Novel DPP-IV Inhibitors

本文详述了一项针对二肽基肽酶4 (DPP4)靶点的创新药物研发策略，该策略通过整合虚拟筛选、分子对接与分子动力学模拟三大前沿技术，实现了药物研发效率与精度的双重提升。研究从大型化合物库出发，运用先进算法与机器学习工具高效筛选出与DPP4靶点具有潜在高结合亲和力的候选分子，大幅缩减实验候选范围。随后，采用分子对接技术深入剖析候选分子与靶点的相互作用模式、能量特征及关键氨基酸残基的作用机制，成功筛选出高亲和力、高选择性的先导化合物。为进一步验证与优化，研究引入分子动力学模拟，动态观测候选药物在生物环境中的构象稳定性、溶剂化效应及与靶点的动态结合，为药物设计提供了科学依据，加速了DPP4抑制剂的研发进程，为糖尿病及其他代谢性疾病的治疗开辟了新前景。
This paper details an innovative drug development strategy targeting dipeptidyl peptidase-4 (DPP4), which integrates three cutting-edge technologies: virtual screening, molecular docking, and molecular dynamics simulations. This strategy enhances both the efficiency and accuracy of drug development. The study begins with a large compound library, employing advanced algorithms and machine learning tools to efficiently screen for candidate molecules with potential high binding affinity to the DPP4 target, significantly narrowing down the pool of experimental candidates. Subsequently, molecular docking techniques are used to deeply analyze the interaction patterns, energy characteristics, and the role of key amino acid residues between the candidate molecules and the target, successfully identifying lead compounds with high affinity and selectivity. To further validate and optimize, the study incorporates molecular dynamics simulations to dynamically observe the conformational stability, solvation effects, and dynamic binding with the target in a biological environment. This provides scientific evidence for drug design, accelerates the development of DPP4 inhibitors, and opens new prospects for the treatment of diabetes and other metabolic diseases.