Abstract：
BACKGROUND: Recent studies have found that Phosphodiesterase-4 (PDE4) is closely related to the pathogenesis of depression, cognitive impairment and neurological impairment.
OBJECTIVE: Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases.
METHODS: We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors.
RESULTS: The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research.
CONCLUSIONS: For the continued development of PDE4D PET radioligand, these models and new compounds\' developing methodology offer a theoretical foundation and crucial references.
OBJECTIVE: Our objective is to develop potent inhibitors of the high-affinity phosphodiesterase 4D isoform (PDE4D) that can serve as radioligands for Positron Emission Tomography (PET) imaging, thereby advancing research in the field of neurological diseases.
METHODS: We employed a multi-step approach combining three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, classification techniques, and CoMSIA analysis to investigate the conformational relationship of highaffinity PDE4D inhibitors as PET ligands. ADMET and Drug-likeness predictions were also conducted. By utilizing these methods, our aim was to identify more potent PDE4D inhibitors.
RESULTS: The results showed that the CoMSIA model with the best principal component scores (n=7) had a cross-validated Q2 value of 0.602 and a non-cross-validated R2 value of 0.976. These results affirmed the excellent predictive capability of the established CoMSIA model. Analysis of the generated 3D-QSAR contour plots highlighted specific regions in the molecular structure of the compounds that can be further optimized and modified. Guided by the contour plots, we designed 100 novel PDE4D inhibitors, and molecular docking was performed for the top 4 compounds with high activity. The molecular docking scores were promising, and ADMET and drug similarity predictions yielded satisfactory results. Taking into consideration these factors, compound 51c was determined to be the optimal compound, laying a solid foundation for further research.
CONCLUSIONS: For the continued development of PDE4D PET radioligand, these models and new compounds\' developing methodology offer a theoretical foundation and crucial references.
摘要：
背景：最近的研究发现磷酸二酯酶-4（PDE4）与抑郁症的发病机制密切相关。认知障碍和神经功能缺损。
目的：我们的目标是开发高亲和力磷酸二酯酶4D同工型（PDE4D）的有效抑制剂，可作为正电子发射断层扫描（PET）成像的放射性配体，从而推进神经疾病领域的研究。
方法：我们采用了结合三维定量结构-活性关系（3D-QSAR）建模的多步骤方法，分子对接,分类技术，和CoMSIA分析，以研究作为PET配体的highaffityPDE4D抑制剂的构象关系。还进行了ADMET和药物相似性预测。通过使用这些方法，我们的目标是确定更有效的PDE4D抑制剂。
结果：结果表明，具有最佳主成分得分（n=7）的CoMSIA模型的交叉验证Q2值为0.602，非交叉验证R2值为0.976。这些结果证实了所建立的CoMSIA模型的优异预测能力。对生成的3D-QSAR等高线图的分析突出了化合物分子结构中可以进一步优化和修饰的特定区域。在等高线图的引导下，我们设计了100种新型PDE4D抑制剂，并对活性最高的4个化合物进行了分子对接。分子对接得分很有希望，ADMET和药物相似性预测取得了令人满意的结果。考虑到这些因素，化合物51c被确定为最佳化合物，为进一步研究奠定了坚实的基础。
结论：对于PDE4DPET放射性配体的持续开发，这些模型和新化合物的开发方法提供了理论基础和重要参考。
目的：我们的目标是开发高亲和力磷酸二酯酶4D同工型（PDE4D）的有效抑制剂，可作为正电子发射断层扫描（PET）成像的放射性配体，从而推进神经疾病领域的研究。
方法：我们采用了结合三维定量结构-活性关系（3D-QSAR）建模的多步骤方法，分子对接,分类技术，和CoMSIA分析，以研究作为PET配体的highaffityPDE4D抑制剂的构象关系。还进行了ADMET和药物相似性预测。通过使用这些方法，我们的目标是确定更有效的PDE4D抑制剂。
结果：结果表明，具有最佳主成分得分（n=7）的CoMSIA模型的交叉验证Q2值为0.602，非交叉验证R2值为0.976。这些结果证实了所建立的CoMSIA模型的优异预测能力。对生成的3D-QSAR等高线图的分析突出了化合物分子结构中可以进一步优化和修饰的特定区域。在等高线图的引导下，我们设计了100种新型PDE4D抑制剂，并对活性最高的4个化合物进行了分子对接。分子对接得分很有希望，ADMET和药物相似性预测取得了令人满意的结果。考虑到这些因素，化合物51c被确定为最佳化合物，为进一步研究奠定了坚实的基础。
结论：对于PDE4DPET放射性配体的持续开发，这些模型和新化合物的开发方法提供了理论基础和重要参考。
