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.

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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.

Histone methyltransferases (HMTs) transfer a methyl group from the cofactor S-adenosyl methionine to lysine or arginine residues on histone tails, thereby regulating chromatin compaction, binding of effector proteins and gene transcription. HMTs constitute an emerging target class in diverse disease areas, and selective chemical probes are necessary for target validation. Potent and selective competitors of the substrate peptide have been reported, but the chemical tractability of the cofactor binding site is poorly understood. Here, a systematic analysis of this site across structures of 14 human HMTs or close homologues was conducted. The druggability, interaction hotspots, and diversity of the cofactor binding pocket were dissected. This analysis strongly suggests that this site is chemically tractable. General principles underlying tight binding and specific guidelines to achieve selective inhibition are presented.

A new consensus approach has been developed for ligand-based virtual screening. It involves combining highly disparate properties in order to improve performance in virtual screening. The properties include structural, 2D pharmacophore and property-based fingerprints, scores derived using BCUT descriptors, and 3D pharmacophore approaches. Different approaches for the combination of all or some of these methods have been tested. Logistic regression and sum ranks were found to be the most advantageous in different pharmaceutical applications. The three major reasons consensus scoring appears to enrich data sets better than single scoring functions are (1) using multiple scoring functions is similar to repeated samplings, in which case the mean is closer to the true value than any single value, (2) due to the better clustering of actives, multiple sampling will recover more actives than inactives, and (3) different methods seem to agree more on the ranking of the actives than on the inactives. Furthermore, consensus results are not only better but are also more consistent across receptor systems.