Biomedical subjects

Modest von Korff

Publications and source records attributed to Modest von Korff.

3 recordsLinked to original sources

PubMedDate not supplied

GPCR-tailored pharmacophore pattern recognition of small molecular ligands.

The goal of our work was to differentiate between patterns, which are responsible for the activity of small molecular ligands binding to G-protein coupled receptors (GPCRs) and molecules, which are pharmacologically active on other target classes. Second the aim was to go one step further and analyze the chemical space occupied by GPCR active ligands itself, to distinguish between the actives of different subclasses or even cluster ligands for single receptors. To achieve these objectives, we have built a database of small, organic molecules, which bind to GPCRs. Once this crucial foundation for pattern recognition has been laid, we needed to find a descriptor, which is able to detect the compulsory features responsible for activity within a molecule. In this matter we found that the well accepted pharmacophore descriptor served us well. Finally we needed to find a method to display the clustering or separation of the specific ligands. We found that self-organizing maps (SOMs) perform excellently in this task. We herein present the analysis of the chemical space of active compounds, depending on their biological target, the GPCRs. We will also discuss the techniques used to create the chemical spaces. The findings can be applied and have an impact at various stages of the drug discovery process.

Ligands↗

PubMedDate not supplied

Toxicity-indicating structural patterns.

We describe a toxicity alerting system for uncharacterized compounds, which is based upon comprehensive tables of substructure fragments that are indicative of toxicity risk. These tables were derived computationally by analyzing the RTECS database and the World Drug Index. We provide, free of charge, a Java applet for structure drawing and toxicity risk assessment. In an independent investigation, we compared the toxicity classification performance of naive Bayesian clustering, k next neighbor classification, and support vector machines. To visualize the chemical space of both toxic and druglike molecules, we trained a large self-organizing map (SOM) with all compounds from the RTECS database and the IDDB. In summary, we found that a support vector machine performed best at classifying compounds of defined toxicity into appropriate toxicity classes. Also, SOMs performed excellently in separating toxic from nontoxic substances. Although these two methods are limited to compounds that are structurally similar to known toxic substances, our fragment-based approach extends predictions to compounds that are structurally dissimilar to compounds used in the training set.

Bayes Theorem↗

PubMedDate not supplied

Assessing the predictive power of unsupervised visualization techniques to improve the identification of GPCR-focused compound libraries.

Principal component analysis and self-organizing maps (SOMs) were compared to cluster and visualize the chemical space of a large and diverse data set. The data set comprised about 3000 G-protein-coupled receptor (GPCR) ligands for about 130 receptors and 3000 non-GPCR ligands from the World Drug Index. The molecules were described with a topological pharmacophore point histogram descriptor and a chemical fingerprint descriptor. To assess the predictive power of the clustering, a leave-multiple-out cross validation with k nearest neighbor classification was performed. The results of the classification tests and the visualization showed a clear superiority of the SOM method. SOM correctly divided the data set into two main clusters, one for the GPCR and the other for the non-GPCR ligands. Our results suggest that a continuous GPCR-ligand space exists.

Drug Design↗