Alban Arrault

Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers

SummaryThe data for 3.8 million compounds from structural databases of 32 providers were gathered and stored in a single chemical database. Duplicates are removed using the IUPAC International Chemical Identifier. After this, 2.6 million compounds remain. Each database and the final one were studied in term of uniqueness, diversity, frameworks, ‘drug-like’ and ‘lead–like’ properties. This study also shows that there are more than 87 000 frameworks in the database. It contains 2.1 million ‘drug-like’ molecules among which, more than one million are ‘lead-like’. This study has been carried out using ‘ScreeningAssistant’, a software dedicated to chemical databases management and screening sets generation. Compounds are stored in a MySQL database and all the operations on this database are carried out by Java code. The druglikeness and leadlikeness are estimated with ‘in–house’ scores using functions to estimate convenience to properties; unicity using the InChI code and diversity using molecular frameworks and fingerprints. The software has been conceived in order to facilitate the update of the database. ‘ScreeningAssistant’ is freely available under the GPL license.

Mining collections of compounds with Screening Assistant 2

BackgroundHigh-throughput screening assays have become the starting point of many drug discovery programs for large pharmaceutical companies as well as academic organisations. Despite the increasing throughput of screening technologies, the almost infinite chemical space remains out of reach, calling for tools dedicated to the analysis and selection of the compound collections intended to be screened.ResultsWe present Screening Assistant 2 (SA2), an open-source JAVA software dedicated to the storage and analysis of small to very large chemical libraries. SA2 stores unique molecules in a MySQL database, and encapsulates several chemoinformatics methods, among which: providers management, interactive visualisation, scaffold analysis, diverse subset creation, descriptors calculation, sub-structure / SMART search, similarity search and filtering. We illustrate the use of SA2 by analysing the composition of a database of 15 million compounds collected from 73 providers, in terms of scaffolds, frameworks, and undesired properties as defined by recently proposed HTS SMARTS filters. We also show how the software can be used to create diverse libraries based on existing ones.ConclusionsScreening Assistant 2 is a user-friendly, open-source software that can be used to manage collections of compounds and perform simple to advanced chemoinformatics analyses. Its modular design and growing documentation facilitate the addition of new functionalities, calling for contributions from the community. The software can be downloaded at http://sa2.sourceforge.net/.

A short series of antidiabetic sulfonylureas exhibit multiple ligand PPARgamma-binding patterns.

The present work explores the PPARgamma-activating properties of a series of eight sulfonylureas, using transfection experiments with 293T cells, and rosiglitazone as a reference PPARgamma agonist. In the same time, results from these in vitro experiments are compared to those generated by a sound in silico PPARgamma-ligand docking procedure combined to a simple and astute strategy analysis. The latter consists of building up a dendrogram (decision tree-like diagram) by applying three successive criteria, namely stability, conformational shape and H-binding strength of the docked sulfonylurea or rosiglitazone. This original dendrogram approach avers to be a successful way to account for our biochemical data. It discriminates also various PPARgamma-binding patterns from our small series of compounds. The recognition of these patterns is extremely important because of the extraordinary potentialities of PPARgamma ligands as therapeutic agents in diabetes, cancer, cardiovascular and neurological disorders.

New QSAR Models for Human Cytochromes P450, 1A2, 2D6 and 3A4 Implicated in the Metabolism of Drugs. Relevance of Dataset on Model Development.

Discarding drug-candidates with potential metabolism issues or with significant drug-drug interactions (DDI) is of major importance in drug discovery projects. When a compound is metabolized by catalytic enzymes, especially CYP450 3A4, the risk of DDI is potentially high. [1] An important point in the ADME studies is the characterization of the enzymes involved in the metabolism of drugs. Among these enzymes, the human cytochrome P450 family (CYP), more specifically 1A2, 2C’s, 2D6 and 3A4, are the most intensively studied [2] and QSAR approaches have shown to be valuable for prediction studies. [3] In many of these stud

ADME-Space: a new tool for medicinal chemists to explore ADME properties

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.