Title: Diversity Oriented Virtual Compound Selection Strategy for High Throughput Cell Based Screening of Potential Anticancer Agents
Anna Gulyas-Forro et al
AMRI Hungary et al
The CancerGrid consortium was formed by ten life sciences companies and academic centers in 2007 to carry out a three-year multidisciplinary research program funded by the European Commission (www.cancergrid.eu
). The consortium members work together to develop novel methods to increase the chance of finding potential anticancer agents. Grid-based computing technology is applied to the virtual screening of huge discovery libraries in order to identify promising lead compounds. According to the project plan 30,000 small molecules are selected by various state-of-the-art computational methods, and are then screened in cell-based and target-based assays. This stage will be followed by model development and validation based on the large number of screening data. In order to discover novel chemotypes for anticancer agents, a multi-step virtual screening procedure was developed and carried out on the initial compound set which includes merged collections from repositories of University of Bari (1,458) and AMRI (199,082) leading to a diverse library (30,000) for biological screening. Forty percent of the compounds were selected against specific cancer targets (HSP90, RET, HDAC and MMP), or their known, biologically active ligands by using in silico similarity and 2D/3D target-based methods [1-5]. Another 50% of the compounds were selected using Drug Like Index (DLI)  and strict ADME filters . In order to support future works of HTS as well as QSAR model building, a reference set was selected randomly (5%) and a \"Trojan horse\"-type of counter set (5%) having poor Drug Like Index and ADME properties was also included. We present here the generation of the discovery screening library carried out by the various research groups. CancerGrid is a multinational research project supported by the European Commission under the Framework Program 6 (#LSHC-CT-2006-037559, www.cancergrid.eu
1. Nicolotti, O.; Miscioscia, T. F.; Leonetti, F.; Muncipinto, G.; Carotti, A. J. Chem. Inf. Mod., 2007, 47, 2439-48; 2. Langer, T.; Hoffmann, R. D., Expert Opinion on Drug Discovery 2006, 1(3), 261-267; 3. Tovar, A.; Eckert, H.; Bajorath, J. ChemMedChem. 2007, 2, 208-217; 4. Mestres, J.; Martín-Couce, L.; Gregori-Puigjané, E.; Cases, M.; Boyer S. J. Chem. Inf. Model 2006, 46: 2725-2736; 5. Instant JChem and JChem Calculator plugins, www.chemaxon.com; 6. Rayan, A.; Marcus, D.; Givaty, O.; Barasch, D.; Goldblum, A.; Abstracts of Papers of the American Chemical Society 2005, 230, U1013 . 7. Fontaine, F., Pastor M., Zamora I., Sanz F. J Med Chem. 2005, 48(7):2687-94.
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Title: Assessment of GPCR ligands Diversity Using Molecular Properties-Based Functional Radars and Fragment Analysis.
Ismail Ijjaali, Benjamin Morlon, Elodie Dubus, François Petitet
By collecting chemical structures of ligands, associated targets with all reported in vitro and in vivo pharmacological responses from the literature, we have built a GPCR knowledgebase. This resource could be used for assessing GPCR-associated pharmacological space.
Datasets have been extracted from this knowledgebase, and then filters have been applied. Only non peptide compounds, tested on GPCR wild type targets, with biological activities (EC50, IC50 and Ki) obtained in binding or second messenger protocols were considered. Depending on the target family, activity thresholds have been set, at 10, 100 or 200 nM. Topological molecular descriptors have been calculated using Calculator™ (ChemAxon) for each unique 2D structure. Each molecular dataset addresses a given GPCR class, superfamily, family or receptor type. Analysis carried out includes comparison of active vs inactive and selective vs non selective compounds.
Functional radars have been used as graphic representations of the GPCR-associated pharmacological space to analyze, potency, specificity and selectivity. These representation of pharmacological spaces combined with chemical fragment occurrence –determined by using Fragmenter™ (ChemAxon) provides a powerful strategy to design focused libraries and improve lead profiling.
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Title: Gathering chemical information from the Internet by using JChem Extensions (KNIME)
The Web may be the most effective information-delivery platform ever created. Fortunately, there are numerous chemical information resources for chemical names, structures and properties that are available on the Web. PubChem of the National Institutes of Healths Molecular Libraries Roadmap initiative (http://pubchem.ncbi.nlm.nih.gov/
) is one of them. However, would one of them be useful to our search? We need to ask ourselves if we really want to perform individual searches. What if we were looking for data on several molecules? Nothing would prevent us from doing this in theory, but in practice, this would be too much work. What we\'d really like is to submit a structure query to a single service that will query all of these free databases for us. We investigated whether such a system can be built with JChem Extensions we developed on KNIME, Konstanz Information Miner.
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