Robert Körner

Publications of Robert Körner

• A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition.

  Authors: Sergii Novotarskyi, Iurii Sushko, Robert Körner, Anil Kumar Pandey, Igor V Tetko

  Journal of chemical information and modeling. 06/2011; 51(6):1271-80.

  Prediction of CYP450 inhibition activity of small molecules poses an important task due to high risk of drug-drug interactions. CYP1A2 is an important member of CYP450 superfamily and accounts forPrediction of CYP450 inhibition activity of small molecules poses an important task due to high risk of drug-drug interactions. CYP1A2 is an important member of CYP450 superfamily and accounts for 15% of total CYP450 presence in human liver. This article compares 80 in-silico QSAR models that were created by following the same procedure with different combinations of descriptors and machine learning methods. The training and test sets consist of 3745 and 3741 inhibitors and noninhibitors from PubChem BioAssay database. A heterogeneous external test set of 160 inhibitors was collected from literature. The studied descriptor sets involve E-state, Dragon and ISIDA SMF descriptors. Machine learning methods involve Associative Neural Networks (ASNN), K Nearest Neighbors (kNN), Random Tree (RT), C4.5 Tree (J48), and Support Vector Machines (SVM). The influence of descriptor selection on model accuracy was studied. The benefits of "bagging" modeling approach were shown. Applicability domain approach was successfully applied in this study and ways of increasing model accuracy through use of applicability domain measures were demonstrated as well as fragment-based model interpretation was performed. The most accurate models in this study achieved values of 83% and 68% correctly classified instances on the internal and external test sets, respectively. The applicability domain approach allowed increasing the prediction accuracy to 90% for 78% of the internal and 17% of the external test sets, respectively. The most accurate models are available online at http://ochem.eu/models/Q5747 .

• Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

  Authors: Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Matthias Rupp, Wolfram Teetz, Stefan Brandmaier, Ahmed Abdelaziz, Volodymyr V Prokopenko, Vsevolod Y Tanchuk [......] Dmitriy Chekmarev, Artem Cherkasov, Joao Aires-de-Sousa, Qing-You Zhang, Andreas Bender, Florian Nigsch, Luc Patiny, Antony Williams, Valery Tkachenko, Igor V Tetko

  Journal of computer-aided molecular design. 06/2011; 25(6):533-54.

  The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: theThe Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

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• Predicting the pKa of small molecule.

  Authors: Matthias Rupp, Robert Körner, Igor V Tetko

  Combinatorial chemistry & high throughput screening. 04/2011; 14(5):307-27.

  The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups, commonly expressed as the negative decadic logarithm pKa of the acidThe biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups, commonly expressed as the negative decadic logarithm pKa of the acid dissociation constant (Ka). We survey the literature on computational methods to predict the pKa of small molecules. In this, we address data availability (used data sets, data quality, proprietary versus public data), molecular representations (quantum mechanics, descriptors, structured representations), prediction methods (approaches, implementations), as well as pKa-specific issues such as mono- and multiprotic compounds. We discuss advantages, problems, recent progress, and challenges in the field.

• Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set.

  Authors: Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Artem Cherkasov, Jiazhong Li, Paola Gramatica, Katja Hansen, Timon Schroeter, Klaus-Robert Müller [......] Denis Fourches, Eugene Muratov, Alexander Tropsha, Igor Baskin, Dragos Horvath, Gilles Marcou, Christophe Muller, Alexander Varnek, Volodymyr V Prokopenko, Igor V Tetko

  Journal of chemical information and modeling. 10/2010; 50(12):2094-111.

  The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM)The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

• Automatic determination of reaction mappings and reaction center information. 1. The imaginary transition state energy approach.

  Authors: Robert Körner, Joannis Apostolakis

  Journal of chemical information and modeling. 07/2008; 48(6):1181-9.

  Chemical reactions transform the reactant molecules by deleting existing and forming new bonds. The identification of these so-called reacting bonds is important for studying the reaction mechanismChemical reactions transform the reactant molecules by deleting existing and forming new bonds. The identification of these so-called reacting bonds is important for studying the reaction mechanism and for applications in metabolomics, e.g. for interpreting substrate labeling experiments. Here, we introduce an approach which suggests the simplest possible reaction center at the heavy atom level, with high accuracy. In contrast to current methods the approach is motivated by a simple theoretical model based on a crude approximation of the reaction energetics, and takes the complete reacting system into account. Finally, it recovers all optimal solutions to the problem while removing all symmetry-related, redundant solutions. We apply the method on the complete KEGG database of biochemical reactions, and compare our approach with previous methods. The resulting reaction centers are represented as imaginary transition states, which are molecule-like representations of reaction mechanisms. We provide the statistics of the calculations on the KEGG database and discuss some examples for the different types of alternative solutions found.

• Automatic determination of reaction mappings and reaction center information. 2. Validation on a biochemical reaction database.

  Authors: Joannis Apostolakis, Oliver Sacher, Robert Körner, Johann Gasteiger

  Journal of chemical information and modeling. 07/2008; 48(6):1190-8.

  The correct identification of the reacting bonds and atoms is a prerequisite for the analysis of the reaction mechanism. We have recently developed a method based on the Imaginary Transition StateThe correct identification of the reacting bonds and atoms is a prerequisite for the analysis of the reaction mechanism. We have recently developed a method based on the Imaginary Transition State Energy Minimization approach for automatically determining the reaction center information and the atom-atom mapping numbers. We test here the accuracy of this ITSE approach by comparing the predictions of the method against more than 1500 manually annotated reactions from BioPath, a comprehensive database of biochemical reactions. The results show high agreement between manually annotated mappings and computational predictions (98.4%), with significant discrepancies in only 24 cases out of 1542 (1.6%). This result validates both the computational prediction and the database, at the same time, as the results of the former agree with expert knowledge and the latter appears largely self-consistent, and consistent with a simple principle. In 10 of the discrepant cases, simple chemical arguments or independent literature studies support the predicted reaction center. In five reaction instances the differences in the automatically and manually annotated mappings are described in detail. Finally, in approximately 200 cases the algorithm finds alternate reaction centers, which need to be studied on a case by case basis, as the exact choice of the alternative may depend on the enzyme catalyzing the reaction.