Biomedical subjects

Johann Gasteiger

Publications and source records attributed to Johann Gasteiger.

26 records · Page 2Linked to original sources

PubMedDate not supplied

Chirality codes and molecular structure.

Some time ago a structure-descriptor, named "chirality code", was put forward [J. Chem. Inf. Comput. Sci. 2001, 41, 369-375], aimed at distinguishing between enantiomers. The chirality code is a sequence of (typically 100) numbers, being equal to the value of a certain "chirality function" at equidistant points within a chosen interval. For molecules of moderate size the chirality function has thousands of peaks (maxima and minima), one for each quartet of atoms. Therefore it looks as if the chirality code cannot provide a faithful representation of the chirality function and thus a faithful representation of the molecular structure. We now show that functional groups present in the molecule result in clusters of near-lying and partially overlapping peaks, whose position in the chirality code is characteristic for the particular functional group. This enables a sound structural interpretation of the chirality code.

Journal Article↗

PubMedDate not supplied

Mining high-throughput screening data of combinatorial libraries: development of a filter to distinguish hits from nonhits.

Kohonen neural networks generate projections of large data sets defined in high-dimensional space. The resulting self-organizing maps can be used in many applications in the drug discovery process, such as to analyze combinatorial libraries for their similarity or diversity and to select descriptors for structure-activity relationships. The ability to investigate thousands of compounds in parallel also allows one to conduct a study based on single-dose experiments of high-throughput screening campaigns, which are known to have a greater uncertainty than IC50 or Ki values. This is demonstrated here for a data set of 5513 compounds from one combinatorial library. Furthermore, a method was developed that uses self-organizing maps not only as an indicator of structure-activity relationships, but as the basis of a classification system allowing predictive modeling of combinatorial libraries.

Journal Article↗

PubMedDate not supplied

A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules.

In this paper we propose a novel graph-based genetic algorithm for the evolution of novel molecular graphs from a predefined set of elements or molecular fragments with an external objective function. A brief overview of existing genetic algorithm approaches in molecular design is provided followed by a description of our approach. The paper continues to suggest a novel application of this program to the multiobjective evolution of median molecules that are structurally representative of a set of objective molecules. We conclude with a summary of our initial results along with a discussion of a variety of improvements and applications of our approach.

Algorithms↗

PubMedDate not supplied

Use of structure descriptors to discriminate between modes of toxic action of phenols.

Two classification models were developed based on a data set of 220 phenols with four associated Modes of Toxic Action (MOA). Counter-propagation neural networks (CPG NN) and multinomial logistic regression (multinom) were used as classification methods. The combination of topological autocorrelation of empirical pi-charge and sigma-electronegativity and of surface autocorrelation of hydrogen-bonding potential resulted in a 21-dimensional model that successfully discriminated between the four MOAs. Its overall predictive power was estimated to 92% using 5-fold cross-validation. Subsequently, a simple score for the distance to the training data was used to determine the prediction space of the model and used in an exploratory study on the phenols contained in the open NCI database. The use of a prediction space metric proved indispensable for the screening of such a diverse database. The prediction space covered by the proposed model is still of rather local nature which is either caused by the limited diversity and size of the training set or by the high dimensionality of the descriptors.

Animals↗

PubMedDate not supplied

Impact of conformational flexibility on three-dimensional similarity searching using correlation vectors.

Many three-dimensional (3D) virtual screening concepts, like automated docking or pharmacophore searching, rely on the calculation of a "bioactive" or "receptor-relevant" conformation of a molecule to assess its biological activity. We investigated the dependence of the presence of conformations near the "bioactive" conformation on three-dimensional similarity searching with pharmacophore-based correlation vectors (CATS3D approach). Cocrystal structures of 11 target classes served as queries for virtual screening of a database of annotated ligands. Different numbers of conformations were calculated. Single 3D structures were obtained using the 3D structure generator CORINA and conformational ensembles by the conformation generation program ROTATE. This approach was able to reproduce conformations for high resolution cocrystal structures. For virtual screening we found that using only the CORINA-generated single conformation already resulted in a significant enrichment of isofunctional molecules having the same biological property profile. This observation was also made for ligand classes with many rotatable bonds. Although more similar conformations were considered to be more similar in the CATS3D description, the impact of using multiple conformations on the enrichment of actives was not as high as expected. CATS3D provides an alignment-free three-dimensional virtual screening approach that is less dependent on the presence of conformations which are close to the "bioactive" conformation of a molecule compared to methods that rely on an explicit three-dimensional alignment of molecules.

Algorithms↗

PubMedDate not supplied

InfVis--platform-independent visual data mining of multidimensional chemical data sets.

The tremendous increase of chemical data sets, both in size and number, and the simultaneous desire to speed up the drug discovery process has resulted in an increasing need for a new generation of computational tools that assist in the extraction of information from data and allow for rapid and in-depth data mining. During recent years, visual data mining has become an important tool within the life sciences and drug discovery area with the potential to help avoiding data analysis from turning into a bottleneck. In this paper, we present InfVis, a platform-independent visual data mining tool for chemists, who usually only have little experience with classical data mining tools, for the visualization, exploration, and analysis of multivariate data sets. InfVis represents multidimensional data sets by using intuitive 3D glyph information visualization techniques. Interactive and dynamic tools such as dynamic query devices allow real-time, interactive data set manipulations and support the user in the identification of relationships and patterns. InfVis has been implemented in Java and Java3D and can be run on a broad range of platforms and operating systems. It can also be embedded as an applet in Web-based interfaces. We will present in this paper examples detailing the analysis of a reaction database that demonstrate how InfVis assists chemists in identifying and extracting hidden information.

Computational Biology↗

PubMedDate not supplied

Query generation to search for inhibitors of enzymatic reactions.

A method for the generation of intermediates of enzyme-catalyzed reactions is presented. These intermediates can be used as three-dimensional structural queries for searching for inhibitors of enzymatic reactions. The intermediates can be considered as being structurally quite close to transition-state analogues. For this application, a database containing detailed chemical information on metabolic reactions is used. The likely three-dimensional structure of the intermediates of enzyme-catalyzed reactions can be generated from the information in the database. For three reactions catalyzed by the enzymes AMP deaminase (EC code 3.5.4.6), triose phosphate isomerase (EC code 5.3.1.1), and arginase II (EC code 3.5.3.1), we show how a 3D model of these intermediates can be superimposed onto known inhibitors of these enzymes by a program that uses a genetic algorithm. For this, we test different methods for the superimposition using information on the enzymatic binding site, using information on physicochemical properties calculated from the molecular structure, or without having any information in the superimposition process. We show that these inhibitors are most similar to the corresponding intermediates regarding the 3D structure.

Algorithms↗

PubMedDate not supplied

Prediction of pKa values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors.

Two quantitative pKa prediction models for aliphatic carboxylic acids and for alcohols were developed by multiple linear-regression (MLR) analysis with empirical atomic descriptors. The acid and alcohol molecules were described by a set of five and four atomic descriptors, respectively. For the pKa model of 1122 aliphatic carboxylic acids, the squared correlation coefficient is 0.813 with a standard error of prediction of 0.423; for the pKa model of 288 alcohols, the squared correlation coefficient is 0.817 with a standard error of prediction of 0.755, respectively. The good predictive abilities of the models obtained were indicated by both cross-validation and by external validation. An atomic descriptor was developed to model the inductive effect of the neighboring atoms for a central atom in a molecule. The ability of the descriptor to measure the inductive effect of substituent groups was demonstrated by a good correlation of this descriptor with Taft sigma* constants in aliphatic carboxylic acids. It provides a new approach to estimate Taft sigma* constants directly from molecular structures. An algorithm using Kohonen neural networks for splitting a data set into a training set and a test set is also presented.

Alcohols↗