Knowledge-based analysis of multi-potent G-protein coupled receptors ligands.

A large number of chemical structures that interact with G-protein coupled receptors (GPCRs) have been disclosed in patents or published papers. Most of these compounds are selective for a given protein target; however, it is well recognized that some GPCR-drugs interact with multiple targets. Using a literature database, we have identified compounds that act on different GPCRs.

Assessing the chemical diversity of an hsp90 database.

The 90-kDa heat shock protein (hsp90) has emerged as a new, promising target for cancer drug discovery. With the simultaneous disruption of a large range of oncogenic pathways, hsp90 inhibition results in either cytostasis or cell death. Diverse inhibitors of this molecular chaperone are currently under intensive study, and several have reached clinical trials.

Drug repositioning using in silico compound profiling.

Background: Drug repositioning is a current strategy to find new uses for existing drugs, patented or not, and for late-stage candidates that failed for lack of efficacy.

Results: In silico profiling of several marketed drugs (methadone, rapamycin, saquinavir and telmisartan) was performed, exploiting a vast amount of published information. Similar compounds were assessed in terms of target-activity profiles for major drug-target families.

Virtual screening for cytochromes p450: successes of machine learning filters.

Cytochromes P450 (CYPs) are crucial targets when predicting the ADME properties (absorption, distribution, metabolism, and excretion) of drugs in development. Particularly, CYPs mediated drug-drug interactions are responsible for major failures in the drug design process. Accurate and robust screening filters are thus needed to predict interactions of potent compounds with CYPs as early as possible in the process.

Assessing the chemical and biological diversity of an ion channels knowledge database.

The aim of the present work is to assess the chemical and biological diversity of ligands reported in scientific articles or patents to be active against ion channels targets. A specific query of the AurSCOPE Ion Channel knowledge database was constructed to retrieve a set of the most active non-peptide ligands tested in binding or electrophysiology experiments against all ion channel families. A biological activity threshold cutoff expressed by K(i), IC(50), or EC(50) was set to 300 nM.

Ligand-based virtual screening to identify new T-type calcium channel blockers.

T-type calcium channels are involved in the generation of rhythmical firing patterns in the mammalian central nervous system and in various pathological alterations of neuronal excitability such as in epilepsy or neuropathic pain. In the search for new T-type calcium channel blockers that would help to treat these disorders, we have followed a bi-dimensional pharmacophore-based virtual screening approach to identify new inhibitors. Nineteen molecules extracted from AurSCOPE Ion Channels knowledgebase were used as query molecules to screen an external database.

Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology.

JNK3 signaling pathway is gaining interest due to its involvement in many neurological disorders. The purpose of this study was to explore for the first time the use of a large and diverse dataset in combination with binary QSAR methodology for predicting JNK3 activity class. Data were extracted from Aureus Pharma' AurSCOPE Kinase knowledge database and active or inactive classes were assigned to ligands based on IC50 biological activity.

Development of an ADME and drug-drug interactions knowledge database for the acceleration of drug discovery and development.

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with.

Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: importance of the quality of the dataset.

The purpose of this study was to explore the use of detailed biological data in combination with a statistical learning method for predicting the CYP1A2 and CYP2D6 inhibition. Data were extracted from the Aureus-Pharma highly structured databases which contain precise measures and detailed experimental protocol concerning the inhibition of the two cytochromes. The methodology used was Recursive Partitioning, an easy and quick method to implement.

In silico classification of HERG channel blockers: a knowledge-based strategy.

The blockage of the hERG potassium channel by a wide number of diverse compounds has become a major pharmacological safety concern as it can lead to sudden cardiac death. In silico models can be potent tools to screen out potential hERG blockers as early as possible during the drug-discovery process. In this study, predictive models developed using the recursive partitioning method and created using diverse datasets from 203 molecules tested on the hERG channel are described.