BioSolveIT GmbH

Neustadt an der Weinstraße, Germany

BioSolveIT GmbH

Neustadt an der Weinstraße, Germany
Time filter
Source Type

Scharfer C.,University of Hamburg | Schulz-Gasch T.,F.Hoffmann LaRoche Ltd. | Hert J.,F.Hoffmann LaRoche Ltd. | Heinzerling L.,University of Hamburg | And 4 more authors.
ChemMedChem | Year: 2013

The generation of sets of low-energy conformations for a given molecule is a central task in drug design. Herein we present a new conformation generator called CONFECT that builds on our previously published library of torsion patterns. Conformations are generated as well as ranked by means of normalized frequency distributions derived from the Cambridge Structural Database (CSD). Following an incremental construction approach, conformations are selected from a systematic enumeration within energetic boundaries. The new tool is benchmarked in several different ways, indicating that it allows the efficient generation of high-quality conformation ensembles. These ensembles are smaller than those produced by state-of-the-art tools, yet they effectively cover conformational space. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bietz S.,University of Hamburg | Urbaczek S.,University of Hamburg | Schulz B.,University of Hamburg | Schulz B.,BioSolveIT GmbH | Rarey M.,University of Hamburg
Journal of Cheminformatics | Year: 2014

The calculation of hydrogen positions is a common preprocessing step when working with crystal structures of protein-ligand complexes. An explicit description of hydrogen atoms is generally needed in order to analyze the binding mode of particular ligands or to calculate the associated binding energies. Due to the large number of degrees of freedom resulting from different chemical moieties and the high degree of mutual dependence this problem is anything but trivial. In addition to an efficient algorithm to take care of the complexity resulting from complicated hydrogen bonding networks, a robust chemical model is needed to describe effects such as tautomerism and ionization consistently. We present a novel method for the placement of hydrogen coordinates in protein-ligand complexes which takes tautomers and protonation states of both protein and ligand into account. Our method generates the most probable hydrogen positions on the basis of an optimal hydrogen bonding network using an empirical scoring function. The high quality of our results could be verified by comparison to the manually adjusted Astex diverse set and a remarkably low rate of undesirable hydrogen contacts compared to other tools. © 2014 Bietz et al.; licensee Chemistry Central Ltd.

Tzvetkov N.T.,University of Bonn | Hinz S.,University of Bonn | Kuppers P.,University of Bonn | Gastreich M.,BioSolveIT GmbH | Muller C.E.,University of Bonn
Journal of Medicinal Chemistry | Year: 2014

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, IC 50 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. © 2014 American Chemical Society.

Kiss R.,Gedeon Richter Plc | Sandor M.,Gedeon Richter Plc | Gere A.,Gedeon Richter Plc | Schmidt E.,Gedeon Richter Plc | And 5 more authors.
Journal of Chemical Information and Modeling | Year: 2012

Ligand-based approaches are particularly important in the hit identification process of drug discovery when no structural information on the target is available. Pharmacophore descriptors that use a topological representation of the ligands are usually fast enough to screen large compound libraries effectively when seeking novel lead candidates. One example of this kind is the Feature Tree descriptor, a reduced graph representation implemented in the FTrees software. In this study, we tested the screening efficiency of FTrees by both retrospective and prospective screens using known histamine H4 antagonists and serotonin transporter (SERT) inhibitors as query molecules. Our results demonstrate that FTrees can effectively find actives. Particularly when combined with a subsequent 2D fingerprint-based diversity selection, FTrees was found to be extremely effective at discovering a diverse set of scaffolds. Prospective screening of our in-house compound deck provided several novel H4 and SERT ligands that could serve as suitable starting points for further optimization. © 2011 American Chemical Society.

Schneider N.,University of Hamburg | Hindle S.,BioSolveIT GmbH | Lange G.,Bayer AG | Klein R.,Bayer AG | And 7 more authors.
Journal of Computer-Aided Molecular Design | Year: 2012

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. © Springer Science+Business Media B.V. 2011.

Schneider N.,University of Hamburg | Lange G.,Bayer AG | Hindle S.,BioSolveIT GmbH | Klein R.,Bayer AG | Rarey M.,University of Hamburg
Journal of Computer-Aided Molecular Design | Year: 2013

The estimation of free energy of binding is a key problem in structure-based design. We developed the scoring function HYDE based on a consistent description of HYdrogen bond and DEhydration energies in protein-ligand complexes. HYDE is applicable to all types of protein targets since it is not calibrated on experimental binding affinity data or protein-ligand complexes. The comprehensible atom-based score of HYDE is visualized by applying a very intuitive coloring scheme, thereby facilitating the analysis of protein-ligand complexes in the lead optimization process. In this paper, we have revised several aspects of the former version of HYDE which was described in detail previously. The revised HYDE version was already validated in large-scale redocking and screening experiments which were performed in the course of the Docking and Scoring Symposium at 241st ACS National Meeting. In this study, we additionally evaluate the ability of the revised HYDE version to predict binding affinities. On the PDBbind 2007 coreset, HYDE achieves a correlation coefficient of 0.62 between the experimental binding constants and the predicted binding energy, performing second best on this dataset compared to 17 other well-established scoring functions. Further, we show that the performance of HYDE in large-scale redocking and virtual screening experiments on the Astex diverse set and the DUD dataset respectively, is comparable to the best methods in this field. © 2012 Springer Science+Business Media Dordrecht.

Loading BioSolveIT GmbH collaborators
Loading BioSolveIT GmbH collaborators