Syntheses, pharmacological evaluation and molecular modelling of substituted 6-alkoxyimidazo[1,2-b]pyridazines as new ligands for the benzodiazepine receptor

N,N-Dialkyl-N′-Chlorosulfonyl Chloroformamidines in Heterocyclic Synthesis. III. Pyrido- and Pyridazo-Fused [1,2,4,6]Thiatriazine Dioxides

N,N-Dialkyl-N′-chlorosulfonylchloroformamidines 1 reacted with 2-aminopyridines 2 to give novel pyrido[1,2-b][1,2,4,6]thiatriazine dioxides 3 and pyrido[2,1-c][1,2,4,6]thiatriazine dioxides 4. Reaction of 1 with 3-aminopyridazines 5 afforded pyridazo[3,2-c][1,2,4,6]thiatriazine dioxides 6 and a pyridazo[2,3-b][1,2,4,6]thiatriazine dioxide 7. The compounds 6 and 7 are derivatives of new ring systems.

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.

Synthesis, Pharmacology, and Structure−Activity Relationships of Novel Imidazolones and Pyrrolones as Modulators of GABAA Receptors

New series of imidazolones and pyrrolones were synthesized. The compounds were tested regarding their anxiolytic properties due to modulation of the GABAA receptor response. Several derivatives exhibit considerable pharmacological activity while lacking the typical side effects of benzodiazepine receptor agonists. 1-(4-chlorophenyl)-4-morpholin-1-yl-1,5-dihydro-imidazol-2-one (2) and 1-(4-chlorophenyl)-4-piperidin-1-yl-1,5-dihydro-imidazol-2-one (3) were protective in the pentylenetetrazole test in rats with oral ED50 of 27.4 and 12.8 mg/kg and TD50 (rotarod) of >500 and 265 mg/kg, respectively. The minimum effective dose in the Vogel conflict test was 3 mg/kg for both compounds. Common structure-activity relationship and comparative molecular field analysis models of the various series of derivatives could be established which are in accordance with a GABAA mediated pharmacological action. The findings fit well into an established pharmacophore model. This model is refined by an additional steric restriction feature.

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.

Locally Linear Embedding for dimensionality reduction in QSAR

Current practice in Quantitative Structure Activity Relationship (QSAR) methods usually involves generating a great number of chemical descriptors and then cutting them back with variable selection techniques. Variable selection is an effective method to reduce the dimensionality but may discard some valuable information. This paper introduces Locally Linear Embedding (LLE), a local non-linear dimensionality reduction technique, that can statistically discover a low-dimensional representation of the chemical data. LLE is shown to create more stable representations than other non-linear dimensionality reduction algorithms, and to be capable of capturing non-linearity in chemical data.

Quantitative structure-activity relationship studies using Gaussian processes

A Gaussian process method (GPM) is described and applied to the production of some QSAR models. These models have the potential to solve a number of problems which arise in QSAR modeling in that no parameters have to be supplied and only one hyperparameter is used in finding the optimal solution. The application of the method to QSAR is illustrated using data sets of compounds active at the benzodiazepine and muscarinic receptors as well as the data set of the toxicity of substituted benzenes to the ciliate, Tetrahymena Pyriformis.

Use of automatic relevance determination in QSAR studies using Bayesian neural networks

We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables in modeling the activity data. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors as well as some toxicological data of the effect of substituted benzenes on Tetetrahymena pyriformis is illustrated.

Robust QSAR models using Bayesian regularized neural networks

We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.