Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs

Future Medicinal Chemistry

Volumn 3, Issue 4, 2011, Pages 425-436

  Wassermann, Anne Mai ^a   Bajorath, Jürgen ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOISOSTERISM; CHEMICAL STRUCTURE; CHEMISTRY; DATA MINING; DRUG TRANSFORMATION; LARGE SCALE PRODUCTION; MATCHED MOLECULAR PAIR; MEDICINAL CHEMISTRY; PHYSICAL CHEMISTRY; PRIORITY JOURNAL;

ALGORITHMS; COMBINATORIAL CHEMISTRY TECHNIQUES; DATA MINING; DATABASES, FACTUAL; DRUG DESIGN; LIGANDS; MODELS, MOLECULAR;

EID: 79953659281     PISSN: 17568919     EISSN: None     Source Type: Journal    
DOI: 10.4155/fmc.10.293     Document Type: Article

References (21)

1. Friedman HL. Infuence of isosteric replacements upon biological activity. National Academy of Sciences-National Research Council Publication No. 206. WA, USA 295 (1951).
2. Olesen PH. The use of bioisosteric groups in lead optimization. Curr. Opin. Drug Discov. Devel. 4(4), 471-478 (2001). (Pubitemid 32678536)
3. Langdon SR, Ertl P, Brown N. Bioisosteric replacements and scaffold hopping in lead generation and optimization. Mol. Inf 29(5), 366-385 (2010).
4. Ertl P. In silico identifcation of bioisosteric functional groups. Curr. Opin. Drug Discov. Devel. 10(3), 281-288 (2007). (Pubitemid 46800222)
5. Devereux M, Popelier PL. In silico techniques for the identifcation of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10(6), 657-668 (2010).
6. Holliday JD, Jelfs SP, Willett P, Gedeck P. Calculation of intersubstituent similarity using R-group descriptors. J. Chem. Inf. Comput. Sci. 43(2), 406-411 (2003).
7. Devereux M, Popelier PL, McLay IM. Quantum isostere database: a web-based tool using quantum chemical topology to predict bioisosteric replacements for drug design. J. Chem. Inf. Model. 49(6), 1497-1513 (2009).
8. Sheridan RP. The most common chemical replacements in drug-like compounds. J. Chem. Inf. Comput. Sci. 42(1), 103-108 (2002). (Pubitemid 35355330)
9. Kenny PW, Sadowski J. Structure modifcation in chemical databases. In: Chemoinformatics in Drug Discovery. Oprea TI (Ed.). Wiley-VCH, Weinheim, Germany, 271-285 (2005).
10. Wassermann AM, Bajorath J. Chemical substitutions that introduce activity cliffs across different compound classes and biological targets. J. Chem. Inf. Model. 50(7), 1248-1256 (2010).
11. Molecular Operating Environment; Chemical Computing Group Inc.: Montreal, QC, Canada (2007)
12. Uni Prot Consortium. The universal protein resource (UniProt) in 2010. Nucleic Acids Res. 38(Database issue), D142-D148 (2010).
13. Hussain J, Rea C. Computationally effcient algorithm to identify matched molecular pairs (MMPs) in large data sets. J. Chem. Inf. Model. 50(3), 339-348 (2010).
14. Papadatos G, Muhammad A, Gillet VJ et al. Lead optimization using matched molecular pairs: inclusion of contextual information for enhanced prediction of hERG inhibition, solubility, and lipophilicity J. Chem. Inf. Model. 50(10), 1872-1886 (2010).
15. Bemis GW, Murcko MA. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39(15), 2887-2893 (1996). (Pubitemid 26251026)
16. Scitegic Pipeline Pilot, Student Edition, Version 6.1; Accelrys, Inc.: San Diego, CA, USA (2007).
17. SMIRKS; Daylight Chemical Information Systems, Inc.: Aliso Viejo, CA, USA (2008).
18. Patani GA, LaVoie EJ. Bioisosterism: a rational approach in drug design. Chem. Rev. 96(8), 3147-3176 (1996). (Pubitemid 126641132)
19. Young DC Computational Drug Design: A Guide for Computational and Medicinal Chemists. John Wiley & Sons, NJ, USA 56-67 (2009).
20. Erlenmeyer H, Leo M. On pseudoatoms. Helv. Chim. Acta. 15, 1171-1186 (1932).
21. Grimm HG. On the systematic arrangement of chemical compounds from the perspective of research on atomic composition; and on some challenges in experimental chemistry Naturwissenschaften 17, 557-564 (1929).