A validation study of molecular descriptors for the rational design of peptide libraries

Investigation of a novel molecular descriptor for the lead optimization of 4-aminoquinazolines as vascular endothelial growth factor receptor-2 inhibitors: application for quantitative structure-activity relationship analysis in lead optimization

We investigated the use of infrared vibrational frequency of ligands as a potential novel molecular descriptor in three different molecular target and chemical series. The vibrational energy of a ligand was approximated from the sum of infrared (IR) absorptions of each functional group within a molecule and normalized by its molecular weight (MDIR). Calculations were performed on a set of 4-aminoquinazolines with similar docking scores for the VEGFR2/KDR receptor. 4-Aminoquinazolines with MDIR values ranging 192-196 provided compounds with KDR inhibitory activity. The correlation of KDR inhibitory activity was similarly observed in a separate chemical series, the pyrazolo[1,5-a]pyrimidines. Initial exploration of this molecular descriptor supports a tool for rapid lead optimization in the 4-aminoquinazoline chemical series and a potential method for scaffold hopping in pursuit of new inhibitors.

A physicogenetic method to assign ligand-binding relationships between 7TM receptors

A computational protocol has been devised to relate 7TM receptor proteins (GPCRs) with respect to physicochemical features of the core ligand-binding site as defined from the crystal structure of bovine rhodopsin. The identification of such receptors that already are associated with ligand information (e.g., small molecule ligands with mutagenesis or SAR data) is used to support structure-guided drug design of novel ligands. A case targeting the newly identified prostaglandin D2 receptor CRTH2 serves as a primary example to illustrate the procedure.

Molecular shape diversity of combinatorial libraries: a prerequisite for broad bioactivity

A computational method to rapidly assess and visualize the diversity in molecular shape associated with a given compound set has been developed. Normalized ratios of principal moments of inertia are plotted into two-dimensional triangular graphs and then used to compare the shape space covered by different compound sets, such as combinatorial libraries of varying size and composition. We have further developed a computational method to analyze interset similarity in terms of shape space coverage, which allows the shape redundancy between the different subsets of a given compound collection to be analyzed in a quantitative way. The shape space coverage has been found to originate mainly from the nature and the 3D-geometry (but not the size) of the central scaffold, while the number and nature of the peripheral substituents and conformational aspects were shown to be of minor importance. Substantial shape space coverage has been correlated with broad biological activity by applying the same shape analysis to collections of known bioactive compounds, such as MDDR and the GOLD-set. The aggregate of our results corroborates the intuitive notion that molecular shape is intimately linked to biological activity and that a high degree of shape (hence scaffold) diversity in screening collections will increase the odds of addressing a broad range of biological targets.

Flexsim-R: a virtual affinity fingerprint descriptor to calculate similarities of functional groups

Methods to describe the similarity of fragments occurring in drug-like molecules are of fundamental importance in computational drug design. In the early phase of lead discovery, they can help to select diverse building blocks for combinatorial compound libraries intended for broad screening. In lead optimization, such methods can guide bioisosteric replacements of one functional group by another or serve as descriptors for QSAR calculations. In this paper, we outline the development of a novel 3D descriptor, termed Flexsim-R, which is a further extension of our virtual affinity fingerprint idea. Descriptors are calculated based on docking of small fragments such as building blocks for combinatorial chemistry or functional groups of drug-like molecules into a reference panel of protein binding sites. The method is validated by examining the neighborhood behavior of the affinity fingerprints and by deriving predictive QSAR models for a couple of literature peptide data sets.