Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

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Bacteria are a major cause of infection. To fight disease and growing resistance, research interest is focused on understanding bacterial metabolism. For a detailed evaluation of the involved mechanisms, a precise knowledge of the molecular composition of the bacteria is required. In this article, various vibrational spectroscopic techniques are applied to comprehensively characterize, on a molecular level, bacteria of the strain Staphylococcus epidermidis, an opportunistic pathogen which has evolved to become a major cause of nosocomial infections. IR absorption spectroscopy reflects the overall chemical composition of the cells, with major focus on the protein vibrations. Smaller sample volumes-down to a single cell-are sufficient to probe the overall chemical composition by means of micro-Raman spectroscopy. The nucleic-acid and aromatic amino-acid moieties are almost exclusively explored by UV resonance Raman spectroscopy. In combination with statistical evaluation methods [hierarchical cluster analysis (HCA), principal component analysis (PCA), linear discriminant analysis (LDA)], the protein and nucleic-acid components that change during the different bacterial growth phases can be identified from the in vivo vibrational spectra. Furthermore, tip-enhanced Raman spectroscopy (TERS) provides insight into the surface structures and follows the dynamics of the polysaccharide and peptide components on the bacterial cells with a spatial resolution below the diffraction limit. This might open new ways for the elucidation of host-bacteria and drug-bacteria interactions.

An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.

Metal complexes with N-heterocyclic carbene (NHC) ligands have been widely used in catalytic chemistry and are now increasingly considered for the development of new chemical tools and metal based drugs. Ruthenium complexes of the type (p-cymene)(NHC)RuCl(2) interacted with biologically relevant thiols and selenols, which resulted in the inhibition of enzymes such as thioredoxin reductase or cathepsin B. Pronounced antiproliferative effects could be obtained provided that an appropriate cellular uptake was achieved. Inhibition of tumor cell growth was accompanied by a perturbation of metabolic parameters such as cellular respiration.