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. 2006 Jul 1;34(Web Server issue):W738-44.
doi: 10.1093/nar/gkl065.

FAF-Drugs: free ADME/tox filtering of compound collections

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FAF-Drugs: free ADME/tox filtering of compound collections

Maria A Miteva et al. Nucleic Acids Res. .

Abstract

In silico screening based on the structures of the ligands or of the receptors has become an essential tool to facilitate the drug discovery process but compound collections are needed to carry out such in silico experiments. It has been recognized that absorption, distribution, metabolism, excretion and toxicity (ADME/tox) are key properties that need to be considered early on, even during the database preparation stage. FAF-Drugs is an online service based on Frowns (a chemoinformatics toolkit) that allows users to process their own compound collections via simple ADME/Tox filtering rules such as molecular weight, polar surface area, logP or number of rotatable bonds. SMILES (Simplified Molecular Input Line Entry System), CANSMILES (canonical smiles) or SDF (structure data file) files are required as input and molecules that pass or do not pass the filters are sent back in CANSMILES format. This service should thus help scientists engaging in drug discovery campaigns. Other utilities and several compound collections suitable for in silico screening are available at our site. FAF-Drugs can be accessed at http://bioserv.rpbs.jussieu.fr/FAFDrugs.html.

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Figures

Figure 1
Figure 1
(a) Schema of the FAF-Drugs service. Compound collections in SMILES, CANSMILES or SDF format are needed as input. Users can select a threshold for each investigated physicochemical properties. XlogP calculations are performed with Xtool (see text). Users obtain two output files, one with molecules that pass the filters and the other with compounds that do not pass the filters. A third file with all the computed properties can also be downloaded. Several other utilities are available at FAF-Drugs, these involve online XlogP calculations (38) computed with Xtool, online OpenBabel for file format conversion and implementation of the Java Molecular Editor from Dr P. Ertl (Novartis Pharma AG, Basel, Switzerland) to draw molecules and obtain the corresponding SMILES string. In addition, at FAF-Drugs, users can find five ADME/tox filtered compound collections ready for VLS computations. Three levels of filtering were applied (see our web site for further details) in order to better suit the needs of potential users. The OpenEye's Omega program was used to generate 3D models, either single conformation or up to 50 conformations, for each molecule that passed the ADME/tox filters. The compound collections can be downloaded in Mol2 format or in SMILES format. Other utilities consist of a Test Set that contains six protein targets (PDB format) and about 10 corresponding ligands (Mol2 format, see information about the format at ) to facilitate evaluation of docking/scoring methods and an interface to PASS (43), a program that predicts binding pocket at the surface of a receptor. Many additional tools pertaining to the field of structural bioinformatics are also available at RPBS such as protein electrostatic computations, loop search, solvent accessibility prediction…(see RPBS services). (b) FAF-Drugs results. Four molecules with different physicochemical properties were selected in order to compare FAF-Drugs calculations with other online tools.
Figure 2
Figure 2
Experimental versus computed logP. Correlation between experimental and calculated log P-values for over 100 compounds.

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References

    1. Shoichet B.K. Virtual screening of chemical libraries. Nature. 2004;432:862–865. - PMC - PubMed
    1. Lyne P.D. Structure-based virtual screening: an overview. Drug Discov. Today. 2002;7:1047–1055. - PubMed
    1. Bleicher K.H., Bohm H.J., Muller K., Alanine A.I. Hit and lead generation: beyond high-throughput screening. Nature Rev. Drug Discov. 2003;2:369–378. - PubMed
    1. Jennings A., Tennant M. Discovery strategies in a BioPharmaceutical startup: maximising your chances of success using computational filters. Curr. Pharm. Des. 2005;11:335–344. - PubMed
    1. McConkey B.J., Sobolev V., Edelman M. The performance of current methods in ligand-protein docking. Current Science. 2002;83:845–856.

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