Matter H
Hoechst Marion Roussel AG, Computational Chemistry, Core Research Functions, Frankfurt am Main, Germany.
J Pept Res. 1998 Oct;52(4):305-14. doi: 10.1111/j.1399-3011.1998.tb01245.x.
Important molecular descriptors used for establishing quantitative structure-activity relationships are investigated to classify similar versus dissimilar peptides. When searching new lead structures, synthesizing and testing compounds which are too similar wastes time and resources. In contrast, any lead optimization program requires the investigation of similar compounds to that lead. Thus, it is important to maximize or minimize the structural diversity of peptides to design useful compound libraries for lead finding or lead refinement projects. If a molecular descriptor is a useful measure of similarity for the design of peptide libraries, small differences in this descriptor for a pair of molecules should only translate into small biological differences. Using this paradigm as a basis for descriptor validation, it was possible to rank different molecular descriptors. Those physicochemical descriptors are 2D fingerprints and five experimentally or theoretically derived principal property scales. Some theoretically derived metrics are obtained by computing interaction energies or similarity indices on predefined 3D grid points using canonical conformations for individual amino acids. The resulting 3D data matrices are analyzed using a principal component analysis leading to three principal properties for CoMFA (Comparative Molecular Field Analysis) or CoMSIA (Comparative Molecular Similarity Index Analysis) derived molecular fields. The descriptor validation results reveal the applicability of design tools on peptide data sets. Experimentally derived descriptors, in general, are more acceptable than computationally derived metrics, while the latter provide a statistically valid alternative to characterize novel building blocks. The CoMSIA metrics perform slightly better than the CoMFA-based principal properties, while GRID-based descriptors are always less acceptable.
研究了用于建立定量构效关系的重要分子描述符，以对相似和不相似的肽进行分类。在寻找新的先导结构时，合成和测试过于相似的化合物会浪费时间和资源。相反，任何先导优化程序都需要研究与该先导相似的化合物。因此，最大化或最小化肽的结构多样性对于设计用于先导发现或先导优化项目的有用化合物库很重要。如果一个分子描述符是设计肽库时相似性的有用度量，那么一对分子在这个描述符上的微小差异应该只会转化为微小的生物学差异。以这种范式作为描述符验证的基础，就有可能对不同的分子描述符进行排名。那些物理化学描述符是二维指纹和五个通过实验或理论推导得到的主要性质标度。一些理论推导的度量是通过使用单个氨基酸的标准构象在预定义的三维网格点上计算相互作用能或相似性指数而获得的。使用主成分分析对得到的三维数据矩阵进行分析，从而得到用于比较分子场分析（CoMFA）或比较分子相似性指数分析（CoMSIA）的分子场的三个主要性质。描述符验证结果揭示了设计工具在肽数据集上的适用性。一般来说，实验推导的描述符比计算推导的度量更可接受，而后者为表征新型结构单元提供了一种统计上有效的替代方法。CoMSIA度量的表现略优于基于CoMFA的主要性质，而基于GRID的描述符总是不太可接受。
