The differences between three different compound classes, natural products, molecules from combinatorial synthesis, and drug molecules, were investigated. The major structural differences between natural and combinatorial compounds originate mainly from properties introduced to make combinatorial synthesis more efficient. These include the number of chiral centers, the prevalence of aromatic rings, the introduction of complex ring systems, and the degree of the saturation of the molecule as well as the number and ratios of different heteroatoms. As drug molecules derive from both natural and synthetic sources, they cover a joint area in property space of natural and combinatorial compounds. A PCA-based scheme is presented that differentiates the three classes of compounds. It is suggested that by mimicking certain distribution properties of natural compounds, combinatorial products might be made that are substantially more diverse and have greater biological relevance.

A method is presented for flexibly aligning small molecules. The method accepts a collection of small molecules with 3D coordinates as input and computes a collection of alignments. Each alignment is given a score, which quantifies the quality of the alignment both in terms of internal strain and overlap of molecular features. The results of several computational experiments on pairs of compounds with known binding conformations are used to systematically and objectively tune the parameters for the method. The results indicate the method's utility for the elucidation of pharmacophores and comparative field analysis.

This paper describes the first application of fuzzy c-means clustering for the selection of representatives from assemblies of conformations or alignments. In case of alignments, their quality is taken into account using a weighted c-means scheme, developed in this work. The performance of fuzzy cluster validity measures, such as compactness, partition function, and entropy, are studied on several examples, but the visual 3D representation of data points is shown to be most beneficial in determining the optimum number of clusters. Fuzzy clustering is expected to perform better than crisp clustering methods in cases where there are a significant number of "outliers", such as in molecular dynamics simulations and molecular alignments.

No abstract

No abstract