20 results on '"Kai Sommer"'
Search Results
2. A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses.
- Author
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Florian Flachsenberg, Agnes Meyder, Kai Sommer, Patrick Penner, and Matthias Rarey
- Published
- 2020
- Full Text
- View/download PDF
3. Conformator: A Novel Method for the Generation of Conformer Ensembles.
- Author
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Nils-Ole Friedrich, Florian Flachsenberg, Agnes Meyder, Kai Sommer, Johannes Kirchmair, and Matthias Rarey
- Published
- 2019
- Full Text
- View/download PDF
4. High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.
- Author
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Nils-Ole Friedrich, Agnes Meyder, Christina de Bruyn Kops, Kai Sommer, Florian Flachsenberg, Matthias Rarey, and Johannes Kirchmair
- Published
- 2017
- Full Text
- View/download PDF
5. NAOMInova: Interactive Geometric Analysis of Noncovalent Interactions in Macromolecular Structures.
- Author
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Therese Inhester, Eva Nittinger, Kai Sommer, Pascal Schmidt, Stefan Bietz, and Matthias Rarey
- Published
- 2017
- Full Text
- View/download PDF
6. Benchmarking Commercial Conformer Ensemble Generators.
- Author
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Nils-Ole Friedrich, Christina de Bruyn Kops, Florian Flachsenberg, Kai Sommer, Matthias Rarey, and Johannes Kirchmair
- Published
- 2017
- Full Text
- View/download PDF
7. UNICON: A Powerful and Easy-to-Use Compound Library Converter.
- Author
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Kai Sommer, Nils-Ole Friedrich, Stefan Bietz, Matthias Hilbig 0001, Therese Inhester, and Matthias Rarey
- Published
- 2016
- Full Text
- View/download PDF
8. Optimized Method of 3D Scaffold Seeding, Cell Cultivation, and Monitoring Cell Status for Bone Tissue Engineering
- Author
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Adrian Krolinski, Kai Sommer, Johanna Wiesner, Oliver Friedrich, and Martin Vielreicher
- Published
- 2023
9. A Consistent Scheme for Gradient-Based Optimization of Protein–Ligand Poses
- Author
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Matthias Rarey, Florian Flachsenberg, Kai Sommer, Agnes Meyder, and Patrick Penner
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010304 chemical physics ,Computer science ,General Chemical Engineering ,Locality ,General Chemistry ,Library and Information Sciences ,Stride length ,01 natural sciences ,0104 chemical sciences ,Computer Science Applications ,010404 medicinal & biomolecular chemistry ,Gradient based algorithm ,Broyden–Fletcher–Goldfarb–Shanno algorithm ,0103 physical sciences ,Pose prediction ,Algorithm ,Protein ligand - Abstract
Scoring and numerical optimization of protein-ligand poses is an integral part of docking tools. Although many scoring functions exist, many of them are not continuously differentiable and they are rarely explicitly analyzed with respect to their numerical optimization behavior. Here, we present a consistent scheme for pose scoring and gradient-based pose optimization. It consists of a novel variant of the BFGS algorithm enabling step-length control, named LSL-BFGS (limited step length BFGS), and the empirical JAMDA scoring function designed for pose prediction and good numerical optimizability. The JAMDA scoring function shows a high pose prediction performance in the CASF-2016 docking power benchmark, top-ranking a pose with an RMSD of ≤2 A in about 89% of the cases. The combination of JAMDA scoring with the LSL-BFGS algorithm shows a significantly higher optimization locality (i.e., no excessive movement of poses) than with the classical BFGS algorithm while retaining the characteristically low number of scoring function evaluations. The JAMDA scoring and optimization scheme is freely available for noncommercial use and academic research.
- Published
- 2020
10. A Consistent Scheme for Gradient-Based Optimization of Protein
- Author
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Florian, Flachsenberg, Agnes, Meyder, Kai, Sommer, Patrick, Penner, and Matthias, Rarey
- Subjects
Molecular Docking Simulation ,Benchmarking ,Proteins ,Ligands ,Algorithms ,Protein Binding - Abstract
Scoring and numerical optimization of protein-ligand poses is an integral part of docking tools. Although many scoring functions exist, many of them are not continuously differentiable and they are rarely explicitly analyzed with respect to their numerical optimization behavior. Here, we present a consistent scheme for pose scoring and gradient-based pose optimization. It consists of a novel variant of the BFGS algorithm enabling step-length control, named LSL-BFGS (limited step length BFGS), and the empirical JAMDA scoring function designed for pose prediction and good numerical optimizability. The JAMDA scoring function shows a high pose prediction performance in the CASF-2016 docking power benchmark, top-ranking a pose with an RMSD of ≤2 Å in about 89% of the cases. The combination of JAMDA scoring with the LSL-BFGS algorithm shows a significantly higher optimization locality (i.e., no excessive movement of poses) than with the classical BFGS algorithm while retaining the characteristically low number of scoring function evaluations. The JAMDA scoring and optimization scheme is freely available for noncommercial use and academic research.
- Published
- 2020
11. From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library
- Author
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Rainer Fährrolfes, Kai Sommer, Florian Flachsenberg, Mathias M. von Behren, Therese Inhester, Andrea Volkamer, Agnes Meyder, Thomas Otto, Eva Nittinger, Matthias Hilbig, Stefan Bietz, Florian Lauck, Matthias Rarey, and Karen T. Schomburg
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0301 basic medicine ,Computer science ,Druggability ,Bioengineering ,Bioinformatics ,computer.software_genre ,Applied Microbiology and Biotechnology ,03 medical and health sciences ,Structural bioinformatics ,Software ,Databases, Protein ,Internet ,Virtual screening ,business.industry ,Computational Biology ,General Medicine ,File format ,Visualization ,030104 developmental biology ,Cheminformatics ,Web service ,Software engineering ,business ,computer ,Biotechnology - Abstract
Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus ( http://proteins.plus ) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein–protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de .
- Published
- 2017
12. Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation
- Author
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Vincent Wagner, Kai Sommer, Linda Jantz, Matthias Rarey, Hans Briem, and Clara D. Christ
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0301 basic medicine ,Pharmacology ,Macrocyclic Compounds ,010304 chemical physics ,Molecular model ,Full Paper ,Molecular Structure ,Drug discovery ,Computer science ,drug design ,molecular modeling ,Organic Chemistry ,free energy calculations ,Full Papers ,01 natural sciences ,Biochemistry ,Combinatorial chemistry ,molecular dynamics ,03 medical and health sciences ,030104 developmental biology ,macrocycles ,0103 physical sciences ,Drug Discovery ,Molecular Medicine ,General Pharmacology, Toxicology and Pharmaceutics - Abstract
Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. Computational tools that suggest macrocyclization based on a known binding mode and that estimate the binding affinity of these macrocycles could have a substantial impact on the medicinal chemistry design process. For both tasks, we established a workflow with high practical value. For five diverse pharmaceutical targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying this method to macrocycles designed by LigMac, a search tool for de novo macrocyclization, our results suggest that we have a robust protocol in hand to design macrocycles and prioritize them prior to synthesis.
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- 2017
13. Conformator: A Novel Method for the Generation of Conformer Ensembles
- Author
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Kai Sommer, Nils-Ole Friedrich, Matthias Rarey, Johannes Kirchmair, Agnes Meyder, and Florian Flachsenberg
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Models, Molecular ,Quantitative structure–activity relationship ,Macrocyclic Compounds ,Time Factors ,Computer science ,General Chemical Engineering ,Molecular Conformation ,Quantitative Structure-Activity Relationship ,Library and Information Sciences ,01 natural sciences ,0103 physical sciences ,Cluster Analysis ,Cluster analysis ,Conformational ensembles ,Conformational isomerism ,Quantitative Biology::Biomolecules ,010304 chemical physics ,Significant difference ,General Chemistry ,0104 chemical sciences ,Computer Science Applications ,010404 medicinal & biomolecular chemistry ,Molecular geometry ,Docking (molecular) ,Drug Design ,Pharmacophore ,Algorithm ,Algorithms - Abstract
Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research. acceptedVersion
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- 2019
14. UNICON: A Powerful and Easy-to-Use Compound Library Converter
- Author
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Matthias Hilbig, Stefan Bietz, Matthias Rarey, Kai Sommer, Nils-Ole Friedrich, and Therese Inhester
- Subjects
Models, Molecular ,0301 basic medicine ,Informatics ,Unicon ,Theoretical computer science ,Computer science ,General Chemical Engineering ,Molecular Conformation ,Library and Information Sciences ,computer.software_genre ,Small Molecule Libraries ,03 medical and health sciences ,Software ,Isomerism ,computer.programming_language ,Structure (mathematical logic) ,business.industry ,Programming language ,General Chemistry ,File format ,Computer Science Applications ,Task (computing) ,030104 developmental biology ,Workflow ,Cheminformatics ,Protons ,Line (text file) ,business ,computer - Abstract
The accurate handling of different chemical file formats and the consistent conversion between them play important roles for calculations in complex cheminformatics workflows. Working with different cheminformatic tools often makes the conversion between file formats a mandatory step. Such a conversion might become a difficult task in cases where the information content substantially differs. This paper describes UNICON, an easy-to-use software tool for this task. The functionality of UNICON ranges from file conversion between standard formats SDF, MOL2, SMILES, PDB, and PDBx/mmCIF via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states, and conformer ensembles. For this purpose, UNICON bundles the key elements of the previously described NAOMI library in a single, easy-to-use command line tool.
- Published
- 2016
15. NAOMInext - Synthetically feasible fragment growing in a structure-based design context
- Author
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Matthias Rarey, Florian Flachsenberg, and Kai Sommer
- Subjects
Fragment-based lead discovery ,Molecular Conformation ,computer.software_genre ,Ligands ,01 natural sciences ,Workflow ,03 medical and health sciences ,Structure-Activity Relationship ,Drug Discovery ,030304 developmental biology ,Graphical user interface ,Pharmacology ,0303 health sciences ,Crystallography ,010405 organic chemistry ,business.industry ,Drug discovery ,Chemistry ,Organic Chemistry ,General Medicine ,0104 chemical sciences ,Molecular Docking Simulation ,Docking (molecular) ,Cheminformatics ,Structure based ,Data mining ,business ,computer ,Combinatorial explosion ,Algorithms - Abstract
Since decades de novo design of small molecules is intensively used and fragment-based drug discovery (FBDD) approaches still gain in popularity. Recent publications considering synthetically feasible de novo drug design underline the ongoing need for new methods. Continuous development of algorithms and tools are made, where a combination of intuitive usage, acceptable runtime, and a thoroughly evaluated workflow on large scale data sets is still a curiosity. Here, we present an intuitive approach for constrained synthetically feasible fragment growing. Starting from a fragment within its crystallized structure building blocks are attached via covalent bond formation to build up larger ligands. Iteratively, conformations are generated inside the binding site and scored to find the best suitable one. To cope with the combinatorial explosion of large flexible building blocks a novel dynamic adaptation algorithm is introduced. The technique achieves low runtimes while keeping high accuracies. The developed workflow is evaluated on a large-scale data set of 264 co-crystallized fragments with their corresponding elaborated ligands. Using our approach for fragment-based ligand growing, we were able to generate putative ligands within an RMSD of less than 2 A compared to its crystallized structure. Additionally, we were able to show the benefit of a monolithic tethered docking like methodology compared to state of the art docking. We incorporated our method, NAOMInext, in a clearly arranged graphical user interface that assists the user by defining valuable constraints to improve and accelerate the sampling workflow. In combination with predefined synthetic reaction rules NAOMInext efficiently suggests ideas for the next generation of novel lead compounds.
- Published
- 2018
16. Benchmarking Commercial Conformer Ensemble Generators
- Author
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Christina de Bruyn Kops, Kai Sommer, Nils-Ole Friedrich, Johannes Kirchmair, Matthias Rarey, and Florian Flachsenberg
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0301 basic medicine ,Models, Molecular ,business.industry ,General Chemical Engineering ,Molecular Conformation ,General Chemistry ,Benchmarking ,Library and Information Sciences ,Machine learning ,computer.software_genre ,Molecular conformation ,Distance geometry ,Computer Science Applications ,03 medical and health sciences ,030104 developmental biology ,Robustness (computer science) ,Drug Discovery ,Artificial intelligence ,business ,Algorithm ,computer ,Conformational isomerism ,Mathematics - Abstract
We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf.2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.
- Published
- 2017
17. NAOMInova: Interactive Geometric Analysis of Noncovalent Interactions in Macromolecular Structures
- Author
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Stefan Bietz, Therese Inhester, Eva Nittinger, Matthias Rarey, Pascal Schmidt, and Kai Sommer
- Subjects
0301 basic medicine ,Models, Molecular ,Geometric analysis ,Computer science ,Macromolecular Substances ,General Chemical Engineering ,Molecular Conformation ,Nanotechnology ,Library and Information Sciences ,01 natural sciences ,Set (abstract data type) ,03 medical and health sciences ,User-Computer Interface ,Protein structure ,Computer Graphics ,Non-covalent interactions ,chemistry.chemical_classification ,Computational Biology ,General Chemistry ,computer.file_format ,Protein Data Bank ,0104 chemical sciences ,Computer Science Applications ,010404 medicinal & biomolecular chemistry ,Variable (computer science) ,030104 developmental biology ,chemistry ,User interface ,Biological system ,computer ,Macromolecule - Abstract
Noncovalent interactions play an important role in macromolecular complexes. The assessment of molecular interactions is often based on knowledge derived from statistics on structural data. Within the last years, the available data in the Brookhaven Protein Data Bank has increased dramatically, quantitatively as well as qualitatively. This development allows the derivation of enhanced interaction models and motivates new ways of data analysis. Here, we present a method to facilitate the analysis of noncovalent interactions enabling detailed insights into the nature of molecular interactions. The method is integrated into a highly variable framework enabling the adaption to user-specific requirements. NAOMInova, the user interface for our method, allows the generation of specific statistics with respect to the chemical environment of substructures. The substructures as well as the analyzed set of protein structures can be chosen arbitrarily. Although NAOMInova was primarily made for data exploration in protein-ligand crystal structures, it can be used in combination with any structure collection, for example, analysis of a carbonyl in the neighborhood of an aromatic ring on a set of structures resulting from a MD simulation. Additionally, a filter for different atom attributes can be applied including the experimental support by electron density for single atoms. In this publication, we present the underlying algorithmic techniques of our method and show application examples that demonstrate NAOMInova's ability to support individual analysis of noncovalent interactions in protein structures. NAOMInova is available at http://www.zbh.uni-hamburg.de/naominova .
- Published
- 2017
18. High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators
- Author
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Matthias Rarey, Nils-Ole Friedrich, Kai Sommer, Florian Flachsenberg, Christina de Bruyn Kops, Johannes Kirchmair, and Agnes Meyder
- Subjects
0301 basic medicine ,Models, Molecular ,Informatics ,Time Factors ,Computer science ,General Chemical Engineering ,Pipeline (computing) ,Protein Data Bank (RCSB PDB) ,Molecular Conformation ,Library and Information Sciences ,computer.software_genre ,Ligands ,03 medical and health sciences ,Basic knowledge ,Conformational isomerism ,Platinum ,Proteins ,General Chemistry ,computer.file_format ,Benchmarking ,Protein Data Bank ,Ligand (biochemistry) ,Computer Science Applications ,030104 developmental biology ,Cheminformatics ,Drug Design ,Data mining ,computer - Abstract
We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .
- Published
- 2017
19. 11th German Conference on Chemoinformatics (GCC 2015) : Fulda, Germany. 8-10 November 2015
- Author
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Uli Fechner, Chris de Graaf, Andrew E. Torda, Stefan Güssregen, Andreas Evers, Hans Matter, Gerhard Hessler, Nicola J. Richmond, Peter Schmidtke, Marwin H. S. Segler, Mark P. Waller, Stefanie Pleik, Joan-Emma Shea, Zachary Levine, Ryan Mullen, Karina van den Broek, Matthias Epple, Hubert Kuhn, Andreas Truszkowski, Achim Zielesny, Johannes Fraaije, Ruben Serral Gracia, Stefan M. Kast, Krishna C. Bulusu, Andreas Bender, Abraham Yosipof, Oren Nahum, Hanoch Senderowitz, Timo Krotzky, Robert Schulz, Gerhard Wolber, Stefan Bietz, Matthias Rarey, Markus O. Zimmermann, Andreas Lange, Manuel Ruff, Johannes Heidrich, Ionut Onlia, Thomas E. Exner, Frank M. Boeckler, Marcel Bermudez, Dzmitry S. Firaha, Oldamur Hollóczki, Barbara Kirchner, Christofer S. Tautermann, Andrea Volkamer, Sameh Eid, Samo Turk, Friedrich Rippmann, Simone Fulle, Noureldin Saleh, Giorgio Saladino, Francesco L. Gervasio, Elke Haensele, Lee Banting, David C. Whitley, Jana Sopkova-de Oliveira Santos, Ronan Bureau, Timothy Clark, Achim Sandmann, Harald Lanig, Patrick Kibies, Jochen Heil, Franziska Hoffgaard, Roland Frach, Julian Engel, Steven Smith, Debjit Basu, Daniel Rauh, Oliver Kohlbacher, Jonathan W. Essex, Michael S. Bodnarchuk, Gregory A. Ross, Arndt R. Finkelmann, Andreas H. Göller, Gisbert Schneider, Tamara Husch, Christoph Schütter, Andrea Balducci, Martin Korth, Fidele Ntie-Kang, Stefan Günther, Wolfgang Sippl, Luc Meva’a Mbaze, Conrad V. Simoben, Lydia L. Lifongo, Philip Judson, Jiří Barilla, Miloš V. Lokajíček, Hana Pisaková, Pavel Simr, Natalia Kireeva, Alexandre Petrov, Denis Ostroumov, Vitaly P. Solovev, Vladislav S. Pervov, Nils-Ole Friedrich, Kai Sommer, Johannes Kirchmair, Eugen Proschak, Julia Weber, Daniel Moser, Lena Kalinowski, Janosch Achenbach, Mark Mackey, Tim Cheeseright, Gerrit Renner, Torsten C. Schmidt, Jürgen Schram, Marion Egelkraut-Holtus, Albert van Oeyen, Tuomo Kalliokoski, Denis Fourches, Akachukwu Ibezim, Chika J. Mbah, Umale M. Adikwu, Ngozi J. Nwodo, Alexander Steudle, Brian B. Masek, Stephan Nagy, David Baker, Fred Soltanshahi, Roman Dorfman, Karen Dubrucq, Hitesh Patel, Oliver Koch, Florian Mrugalla, Qurrat U. Ain, Julian E. Fuchs, Robert M. Owen, Kiyoyuki Omoto, Rubben Torella, David C. Pryde, Robert Glen, Petr Hošek, Vojtěch Spiwok, Lewis H. Mervin, Ian Barrett, Mike Firth, David C. Murray, Lisa McWilliams, Qing Cao, Ola Engkvist, Dawid Warszycki, Marek Śmieja, Andrzej J. Bojarski, Natalia Aniceto, Alex Freitas, Taravat Ghafourian, Guido Herrmann, Valentina Eigner-Pitto, Alexandra Naß, Rafał Kurczab, Marcel B. Günther, Susanne Hennig, Felix M. Büttner, Christoph Schall, Adrian Sievers-Engler, Francesco Ansideri, Pierre Koch, Thilo Stehle, Stefan Laufer, Frank M. Böckler, Barbara Zdrazil, Floriane Montanari, Gerhard F. Ecker, Christoph Grebner, Anders Hogner, Johan Ulander, Karl Edman, Victor Guallar, Christian Tyrchan, Wolfgang Klute, Fredrik Bergström, Christian Kramer, Quoc Dat Nguyen, Steven Strohfeldt, Saraphina Böttcher, Tim Pongratz, Dominik Horinek, Bernd Rupp, Raed Al-Yamori, Michael Lisurek, Ronald Kühne, Filipe Furtado, Ludger Wessjohann, Miriam Mathea, Knut Baumann, Siti Zuraidah Mohamad-Zobir, Xianjun Fu, Tai-Ping Fan, Maximilian A. Kuhn, Christoph A. Sotriffer, Azedine Zoufir, Xitong Li, Lewis Mervin, Ellen Berg, Mark Polokoff, Wolf D. Ihlenfeldt, Jette Pretzel, Zayan Alhalabi, Robert Fraczkiewicz, Marvin Waldman, Robert D. Clark, Neem Shaikh, Prabha Garg, Alexander Kos, Hans-Jürgen Himmler, Christophe Jardin, Heinrich Sticht, Thomas B. Steinbrecher, Markus Dahlgren, Daniel Cappel, Teng Lin, Lingle Wang, Goran Krilov, Robert Abel, Richard Friesner, Woody Sherman, Ina A. Pöhner, Joanna Panecka, Rebecca C. Wade, Karen T. Schomburg, Matthias Hilbig, Christian Jäger, Vivien Wieczorek, Lance M. Westerhoff, Oleg Y. Borbulevych, Hans-Ulrich Demuth, Mirko Buchholz, Denis Schmidt, Thomas Rickmeyer, Peter Kolb, Sumit Mittal, Elsa Sánchez-García, Mauro S. Nogueira, Tiago B. Oliveira, Fernando B. da Costa, and Thomas J. Schmidt
- Subjects
0303 health sciences ,Philosophy ,Library and Information Sciences ,16. Peace & justice ,Bioinformatics ,01 natural sciences ,Computer Graphics and Computer-Aided Design ,Meeting Abstracts ,language.human_language ,0104 chemical sciences ,Computer Science Applications ,German ,010404 medicinal & biomolecular chemistry ,03 medical and health sciences ,language ,Physical and Theoretical Chemistry ,Humanities ,030304 developmental biology - Abstract
Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a
- Published
- 2016
20. Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model
- Author
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Janosch Achenbach, Cora Wunder, Alexander Paulke, Ewgenij Proschak, Stefan W. Toennes, and Kai Sommer
- Subjects
0301 basic medicine ,Models, Molecular ,Quantitative structure–activity relationship ,Cannabinoid receptor ,In silico ,Quantitative Structure-Activity Relationship ,Toxicology ,CANNABINOID RECEPTOR 1 ,Cross-validation ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Receptor, Cannabinoid, CB1 ,Predictive Value of Tests ,Synthetic cannabinoids ,medicine ,Computer Simulation ,Chemistry ,Cannabinoids ,Reproducibility of Results ,Biological activity ,General Medicine ,Binding constant ,Combinatorial chemistry ,030104 developmental biology ,030217 neurology & neurosurgery ,Algorithms ,medicine.drug - Abstract
The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.
- Published
- 2015
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