Pharmacy / Drug discovery

Drug discovery

The discovery of active compounds includes different phases of R&D. Greenpharma accompanies customers in the early research stage by proposing innovative services in the library design and the selection of molecules for biological evaluations.

In this end, we use our chemoinformatic platform and the Ambinter & GPDB database as sources for compounds. Depending on the needs and the level of information, the selection may be performed according to structural diversity or similarity. We can also do virtual screening.

Furthermore, we propose tools for drug repurposing such as SELNERGY^TM

Last, ASQR/QSPR approaches are useful for the predictions of biological activities or physico-chemical properties.

Chemical library design

This service may includes medicinal chemistry coupled with chemoinformatics. First, we determine with you the objectives of the library: criteria can be diverse. They may be based on physico-chemical, biochemical properties or others. It will also depends on the stage of project. For example, you want to have a library for your future screening campaign without a priori focus on a particular target, then a chemically diverse library is the most relevant. We will select compounds based on the diversity of their chemical structure: this increases the probability to have hits compared to a classical library. Thanks to our database Ambinter.com, we have access to more than 30 millions of products.

When hits are identified, one may want to explore the structure-activity relationship, therefore selecting compounds based on their structural similarity with the hits are more suitable in that case. Our chemists can also develop libraries based on particular scaffolds.

We may use virtual screening approach to select molecules targeting a specific enzyme, receptor or transporter…

Finally, thanks to our GPDB database, selections can be performed based on other criteria such as molecules from edible plants, molecules belonging to a particular phytochemical family,…

The final products may be delivered in different format (to be defined with the customer): powder, formatted in microplates etc.

Virtual screening

Activity, synergy and selectivity are crucial parameters in lead discovery process. In silico technologies such as virtual screening can accelerate this process by focusing on putative active and selective molecules for a biological property and avoiding ADMET problems in a very early stage.

Virtual screening is therefore an efficient decision making tool for selecting hits to develop into leads. Moreover, identification of new applications for known compounds can be undertaken by this technique.

Greenpharma virtual screening platform consists in:

Greenpharma Database (GPDB) contains 150000 natural compounds; we also have access to the 30 million compounds from Ambinter.com database (mostly synthetic products). These are important sources for virtual screening projects.
GPDB with 10000 protein structures annotated with their source, therapeutic domain and protein family. GPDB also includes chemosensory receptors. This part of GPDB is crucial for inverse docking.
SELNERGY^TM, our in-house virtual screening tool. It is a useful tool to explore interactions between chemical and biological spaces eg chemogenomics.

Inverse docking - Selnergy

This tool was developed at Greenpharma. It consists in a docking software to predict interaction energies of a ligand with a protein and a database of 10 000 protein structures with annotated biological properties – eg antibacterial, anti-inflammatory, aging… The goal of this unique software is to allow in silico biological profiling. It provides predictions on the potential selectivity and/or the synergy that molecules may have on a set of protein targets – hence the name of Selnergy.

Selnergy has proven its usefulness in drug repositioning for Tofisopam [Curr Med Chem. 2008;15(30):3196-203
(link here)]

QSAR

QSAR or QSPR respectively Quantitative Structure Activity or Property Relationship are powerful techniques consisting in using available structural data (translated into molecular descriptors) to derive a mathematical model. This model relates changes in the structure with an activity or a property.

QSAR techniques are successfully used in pharmacy for lead optimisation. QSPR can be applied to build models to estimate properties such as water solubility, logP, toxicity or organoleptic properties based on molecules with known properties.