Literature DB >> 15680586

Assessment of chemical libraries for their druggability.

Suzanne Sirois1, George Hatzakis, Dongqing Wei, Qishi Du, Kuo-Chen Chou.   

Abstract

High throughput virtual screening is acknowledged as the initial means for identifying hit compounds that will be eventually transformed to leads or drug candidates. To improve quality of screening, it is essential to have powerful methods for the analysis of the compound databases. For this purpose, we have developed a novel and practical scoring function to assess the druggability of compounds. The proposed function consists of 12 metrics that take into account physical, chemical and structural properties as well as the presence of undesirable functional groups. We have applied this 12-metric scoring function to 44 different databases that include more than 3.8 million compounds, which are commercially available. The overall quality of each database was evaluated according to the score and rank measured by our 12-metric function. Our findings suggest that, the majority of compounds that do not satisfy druggable rules do so due to high molecular weight, high logP values and the presence of reactive functional groups.

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Year:  2005        PMID: 15680586     DOI: 10.1016/j.compbiolchem.2004.11.003

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  15 in total

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

Authors:  Aurélien Monge; Alban Arrault; Christophe Marot; Luc Morin-Allory
Journal:  Mol Divers       Date:  2006-09-21       Impact factor: 2.943

2.  Mechanism of carbamate inactivation of FAAH: implications for the design of covalent inhibitors and in vivo functional probes for enzymes.

Authors:  Jessica P Alexander; Benjamin F Cravatt
Journal:  Chem Biol       Date:  2005-11

3.  Predicting drug-target interaction networks based on functional groups and biological features.

Authors:  Zhisong He; Jian Zhang; Xiao-He Shi; Le-Le Hu; Xiangyin Kong; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2010-03-11       Impact factor: 3.240

4.  Identification and validation of human DNA ligase inhibitors using computer-aided drug design.

Authors:  Shijun Zhong; Xi Chen; Xiao Zhu; Barbara Dziegielewska; Kurtis E Bachman; Tom Ellenberger; Jeff D Ballin; Gerald M Wilson; Alan E Tomkinson; Alexander D MacKerell
Journal:  J Med Chem       Date:  2008-07-17       Impact factor: 7.446

Review 5.  Rational methods for the selection of diverse screening compounds.

Authors:  David J Huggins; Ashok R Venkitaraman; David R Spring
Journal:  ACS Chem Biol       Date:  2011-02-15       Impact factor: 5.100

6.  Indirect readout in drug-DNA recognition: role of sequence-dependent DNA conformation.

Authors:  Marcos J Araúzo-Bravo; Akinori Sarai
Journal:  Nucleic Acids Res       Date:  2007-11-26       Impact factor: 16.971

7.  iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

Authors:  Xuan Xiao; Jian-Liang Min; Pu Wang; Kuo-Chen Chou
Journal:  PLoS One       Date:  2013-08-27       Impact factor: 3.240

8.  iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

Authors:  Yue-Nong Fan; Xuan Xiao; Jian-Liang Min; Kuo-Chen Chou
Journal:  Int J Mol Sci       Date:  2014-03-19       Impact factor: 5.923

9.  Covalent docking of large libraries for the discovery of chemical probes.

Authors:  Nir London; Rand M Miller; Shyam Krishnan; Kenji Uchida; John J Irwin; Oliv Eidam; Lucie Gibold; Peter Cimermančič; Richard Bonnet; Brian K Shoichet; Jack Taunton
Journal:  Nat Chem Biol       Date:  2014-10-26       Impact factor: 15.040

10.  FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects.

Authors:  David Lagorce; Olivier Sperandio; Hervé Galons; Maria A Miteva; Bruno O Villoutreix
Journal:  BMC Bioinformatics       Date:  2008-09-24       Impact factor: 3.169

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