A VALIDATION-STUDY OF MOLECULAR DESCRIPTORS FOR THE RATIONAL DESIGN OF PEPTIDE LIBRARIES

Important molecular descriptors used for establishing quantitative str ucture-activity relationships are investigated to classify similar ver sus dissimilar peptides. When searching new lead structures, synthesiz ing and testing compounds which are too similar wastes time and resour ces. In contrast, any lead optimization program requires the investiga tion of similar compounds to that lead. Thus, it is important to maxim ize 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 possi ble to rank different molecular descriptors. Those physicochemical des criptors 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 o n predefined 3D grid points using canonical conformations for individu al amino acids. The resulting 3D data matrices are analyzed using a pr incipal component analysis leading to three principal properties for C oMFA (Comparative Molecular Field Analysis) or CoMSIA (Comparative Mol ecular Similarity Index Analysis) derived molecular fields. The descri ptor validation results reveal the applicability of design tools on pe ptide data sets. Experimentally derived descriptors, in general, are m ore acceptable than computationally derived metrics, while the latter provide a statistically valid alternative to characterize novel buildi ng blocks. The CoMSIA metrics perform slighly better than the CoMFA-ba sed principal properties, while GRID-based descriptors are always less acceptable.