Activity landscape characterization has been demonstrated to be a valuable tool in lead optimization, virtual screening, and computational modeling of active compounds. In this work, we present a general protocol to explore systematically the activity landscape of a lead series using 11 2D and 3D structural representations. As a test case we employed a set of 48 bicyclic guanidines (BCGs) with kappa-opioid receptor binding affinity, identified in our group. MACCS keys, graph-based three point pharmacophores, circular fingerprints, ROCS shape descriptors, and the TARIS approach, that compares structures based on molecular electrostatic potentials, were employed as orthogonal descriptors. Based on 'activity cliffs' common to a series of descriptors, we introduce the concept of consensus activity cliffs. Results for the current test case suggest that the presence or absence of a methoxybenzyl group may lead to different modes of binding for the active BCGs with the kappa-opioid receptor. The most active compound (IC50 = 37 nM) is involved in a number of consensus cliffs making it a more challenge query for future virtual screening than would be expected from affinity alone. Results also reveal the importance of screening high density combinatorial libraries, especially in the "cliff-rich" regions of activity landscapes. The protocol presented here can be applied to other data sets to develop a consensus model of the activity landscape.

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A multiple criteria approach is presented, that is used to perform a comparative analysis of four recently developed combinatorial libraries to drugs, Molecular Libraries Small Molecule Repository (MLSMR) and natural products. The compound databases were assessed in terms of physicochemical properties, scaffolds and fingerprints. The approach enables the analysis of property space coverage, degree of overlap between collections, scaffold and structural diversity and overall structural novelty. The degree of overlap between combinatorial libraries and drugs was assessed using the R-NN curve methodology, which measures the density of chemical space around a query molecule embedded in the chemical space of a target collection. The combinatorial libraries studied in this work exhibit scaffolds that were not observed in the drug, MLSMR and natural products collections. The fingerprint-based comparisons indicate that these combinatorial libraries are structurally different to current drugs. The R-NN curve methodology revealed that a proportion of molecules in the combinatorial libraries are located within the property space of the drugs. However, the R-NN analysis also showed that there are a significant number of molecules in several combinatorial libraries that are located in sparse regions of the drug space.

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Enzymes involved in the epigenetic regulation of the genome represent promising starting points for therapeutic intervention by small molecules, and DNA methyltransferases (DNMT) are emerging targets for the development of a new class of cancer therapeutics. In this work, we present nanaomycin A, initially identified by a virtual screening for inhibitors against DNMT1, as a compound inducing antiproliferative effects in three different tumor cell lines originating from different tissues. Nanaomycin A treatment reduced the global methylation levels in all three cell lines and reactivated transcription of the RASSF1A tumor suppressor gene. In biochemical assays, nanaomycin A revealed selectivity toward DNMT3B. To the best of our knowledge, this is the first DNMT3B-selective inhibitor identified to induce genomic demethylation. Our study thus establishes the possibility of selectively inhibiting individual DNMT enzymes. Mol Cancer Ther; 9(11); 3015-23.

Scaffold diversity analysis of compound databases has multiple applications in medicinal chemistry and drug discovery including library design, compounds acquisition, virtual screening and assessment of structure-activity-relationships. The scaffold diversity is commonly measured based on frequency counts. Further information can be obtained by considering the specific distribution of the molecules in those scaffolds. To this end, we introduce in this work the use of an entropy-based information metric to assess the scaffold diversity of compound data sets. As a test case we analyzed the scaffold diversity of 16 data sets of active compounds comparable in size targeting five protein classes of interest in drug design. The diversity was also assessed in terms of frequency counts and scaffold retrieval curves. The entropy-based information metric takes into account the frequency distribution of the different scaffolds and is a complementary measure of scaffold diversity enabling a more comprehensive analysis.

Quantitative Structure-Activity Relationships (QSAR) are based on the hypothesis that changes in molecular structure reflect proportional changes in the observed response or biological activity. In order to successfully conduct QSAR studies certain conditions have to be met that are not frequently reported in the literature. This suggests that some authors are not aware of the principle flaws, occasional shortcomings, and circumstantial downsides of QSAR methods. The present paper focuses on prerequisites to set up correct models and on limitations of model applications. Their implications are systematically described and illustrated as pitfalls that have strong implications in QSAR, and possible solutions are suggested. The paper is focused on small scale 2D- and 3D-QSAR studies for lead optimization. The work is enriched with comprehensive comments and non-mathematical explanations for the computer practitioner in Medicinal Chemistry.