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The HYDE scoring function consistently describes hydrogen bonding, the hydrophobic effect and desolvation. It relies on HYdration and DEsolvation terms which are calibrated using octanol/water partition coefficients of small molecules. We do not use affinity data for calibration, therefore HYDE is generally applicable to all protein targets. HYDE reflects the Gibbs free energy of binding while only considering the essential interactions of protein-ligand complexes. The greatest benefit of HYDE is that it yields a very intuitive atom-based score, which can be mapped onto the ligand and protein atoms. This allows the direct visualization of the score and consequently facilitates analysis of protein-ligand complexes during the lead optimization process. In this study, we validated our new scoring function by applying it in large-scale docking experiments. We could successfully predict the correct binding mode in 93% of complexes in redocking calculations on the Astex diverse set, while our performance in virtual screening experiments using the DUD dataset showed significant enrichment values with a mean AUC of 0.77 across all protein targets with little or no structural defects. As part of these studies, we also carried out a very detailed analysis of the data that revealed interesting pitfalls, which we highlight here and which should be addressed in future benchmark datasets.

In this paper, we present a new algorithm for automated drawing of 2D structural formulas of molecules. The algorithm is based on the classical scheme of a drawing queue placing the molecular fragments in a sequential way. We extend the concept of so-called prefabricated units developed for complex ring systems to automatically created drawing units for chains and rings which will then be assembled in a sequential fashion. The approach is fast and can be naturally extended to the problem of drawing molecules with common core structures. Further on, we present an algorithm that allows the drawing of 2D structural formulas under directional constraints assigned to a subset of bonds. Since no numerical optimization is necessary, the algorithm creates drawings of small organic molecules on the order of 500 structures per second. The new algorithm is relevant for all kinds of prediction and analysis software presenting a large number of probably similar molecular structures to the user of the software.

Indazole- and indole-carboxamides were discovered as highly potent, selective, competitive, and reversible inhibitors of monoamine oxidase B (MAO-B). The compounds are easily accessible by standard synthetic procedures with high overall yields. The most potent derivatives were N-(3,4-dichlorophenyl)-1-methyl-1H-indazole-5-carboxamide (38a, PSB-1491, IC50 human MAO-B 0.386 nM, >25000-fold selective versus MAO-A) and N-(3,4-dichlorophenyl)-1H-indole-5-carboxamide (53, PSB-1410, IC50 human MAO-B 0.227 nM, >5700-fold selective versus MAO-A). Replacement of the carboxamide linker with a methanimine spacer leading to (E)-N-(3,4-dichlorophenyl)-1-(1H-indazol-5-yl)methanimine (58) represents a further novel class of highly potent and selective MAO-B inhibitors (IC50 human MAO-B 0.612 nM, >16000-fold selective versus MAO-A). In N-(3,4-difluorophenyl-1H-indazole-5-carboxamide (30, PSB-1434, IC50 human MAO-B 1.59 nM, selectivity versus MAO-A>6000-fold), high potency and selectivity are optimally combined with superior physicochemical properties. Computational docking studies provided insights into the inhibitors' interaction with the enzyme binding site and a rationale for their high potency despite their small molecular size.

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