Condorcet and borda count fusion method for ligand-based virtual screening

Journal of Cheminformatics

Volumn 6, Issue 1, 2014, Pages

  Ahmed, Ali ^a,b   Saeed, Faisal ^a   Salim, Naomie ^a   Abdo, Ammar ^c  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b KARARY UNIVERSITY   (Sudan)

^c HODEIDAH UNIVERSITY   (Yemen)

Author keywords

Data fusion; Similarity coefficients; Similarity searching; Virtual screening

Indexed keywords

EID: 84901309421     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/1758-2946-6-19     Document Type: Article

References (42)

1. Johnson MA, Maggiora GM, Concepts and Applications of Molecular Similarity New York: Wiley New York 1990
2. Virtual screening-an overview. Walters WP, Stahl MT, Murcko MA, Drug Discov Today 1998 3 4 160 178 10.1016/S1359-6446(97)01163-X
3. Chemical similarity searching. Willett P, Barnard JM, Downs GM, J Chem Inf Comput Sci 1998 38 6 983 996 10.1021/ci9800211 (Pubitemid 128591261)
4. Molecular similarity: a key technique in molecular informatics. Bender A, Glen RC, Org Biomol Chem 2004 2 22 3204 3218 10.1039/b409813g 15534697
5. Similarity methods in chemoinformatics. Willett P, Annual Rev Info Sci Technol 2009 43 1 1 117
6. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. Geppert H, Vogt M, Bajorath JR, J Chem Inf Model 2010 50 2 205 216 10.1021/ci900419k 20088575
7. State-of-the-art in ligand-based virtual screening. Ripphausen P, Nisius B, Bajorath J, Drug Discov Today 2011 16 9 372 376 21349346
8. Similarity-based data mining in files of two-dimensional chemical structures using fingerprint measures of molecular resemblance. Willett P, Wiley Interdis Rev Data Mining Knowled Discov 2011 1 3 241 251 10.1002/widm.26
9. Why do we need so many chemical similarity search methods? Sheridan RP, Kearsley SK, Drug Discov Today 2002 7 17 903 911 10.1016/S1359-6446(02)02411-X 12546933
10. Enhancing the effectiveness of ligand-based virtual screening using data fusion. Willett P, QSAR Comb Sci 2006 25 12 1143 1152 10.1002/qsar.200610084 (Pubitemid 46052451)
11. Consensus scoring for protein-ligand interactions. Feher M, Drug Discov Today 2006 11 9 421 428 16635804 (Pubitemid 44382521)
12. Chemical similarity using physiochemical property descriptors. Kearsley SK, Sallamack S, Fluder EM, Andose JD, Mosley RT, Sheridan RP, J Chem Inf Comput Sci 1996 36 1 118 127 10.1021/ci950274j (Pubitemid 126579143)
13. Chemical similarity using geometric atom pair descriptors. Sheridan RP, Miller MD, Underwood DJ, Kearsley SK, J Chem Inf Comput Sci 1996 36 1 128 136 10.1021/ci950275b (Pubitemid 126579144)
14. Combination of molecular similarity measures using data fusion. Ginn CM, Willett P, Bradshaw J, Perspect Drug Discov Des 2002 20 1 16
15. Enhanced ranking of PknB Inhibitors using data fusion methods. Seal A, Yogeeswari P, Sriram D, Wild DJ, J Cheminfo 2013 5 1 1 11 10.1186/1758-2946-5-1
16. Combination of similarity rankings using data fusion. Willett P, J Chem Inf Model 2013 53 1 1 10 10.1021/ci300547g 23297768
17. Fusing similarity rankings in ligand-based virtual screening. computational and structural. Willett P, Comput Struct Biotechnol J 2013 5 201302002 24688695
18. Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing. Riniker S, Fechner N, Landrum G, J Chem Inf Model 2013 53 11 2829 2836 10.1021/ci400466r 24171408
19. Condorcet Fusion for Improved Retrieval. Montague M, Aslam JA, Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM'02) McLean, Virginia, USA: ACM 2002 538 548
20. Liberalism against Populism: a Confrontation between the Theory of Democracy and the Theory of Social Choice. Riker WH, San Francisco, CA: Freeman San Francisco 1982
21. Measuring the degree of similarity between objects in text retrieval systems. Ellis D, Furner-Hines J, Willett P, Perspec Info Manag 1993 3 2 128 149
22. Sci Tegic Accelrys Inc., the MDL Drug Data Report (MDDR). Symyx Technologies. MDL drug data report, Database is available at http://www.accelrys.com/. (accessed 1st of April 2014)
23. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. Rohrer SG, Baumann K, J Chem Inf Model 2009 49 2 169 184 10.1021/ci8002649 19434821
24. Benchmarking sets for molecular docking. Huang N, Shoichet BK, Irwin JJ, J Med Chem 2006 49 23 6789 6801 10.1021/jm0608356 17154509 (Pubitemid 44749746)
25. SciTegic Accelrys Inc. Pipeline Pilot Software, San Diego: Accelrys Inc 2008 http://www.accelrys.com/
26. New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening. Abdo A, Salim N, J Chem Inf Model 2011 51 25 32 10.1021/ci100232h 21155550
27. Ligand expansion in ligand-based virtual screening using relevance feedback. Abdo A, Saeed F, Hamza H, Ahmed A, Salim N, J Comput Aided Mol Des 2012 26 3 279 287 10.1007/s10822-012-9543-4 22249773
28. Voting-based consensus clustering for combining multiple clusterings of chemical structures. Saeed F, Salim N, Abdo A, J Cheminfo 2012 4 1 1 8 10.1186/1758-2946-4-1
29. Graph-based consensus clustering for combining multiple clusterings of chemical structures. Saeed F, Salim N, Abdo A, Mol Info 2013 32 2 165 178 10.1002/minf.201200110
30. New fragment weighting scheme for the bayesian inference network in ligand-based virtual screening. Abdo A, Salim N, J Chem Inf Model 2010 51 1 25 32 21155550
31. FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set. Cross S, Baroni M, Carosati E, Benedetti P, Clementi S, J Chem Inf Model 2010 50 8 1442 1450 10.1021/ci100221g 20690627
32. Docking performance of the glide program as evaluated on the astex and DUD datasets: a complete set of glide SP results and selected results for a new scoring function integrating WaterMap and glide. Repasky MP, Murphy RB, Banks JL, Greenwood JR, Tubert-Brohman I, Bhat S, Friesner RA, J Comput Aided Mol Des 2012 26 6 787 799 10.1007/s10822-012-9575-9 22576241
33. Siegel S, Nonparametric Statistics for the Behavioral Sciences New York: McGraw-Hill 1956
34. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Friedman M, J Am Stat Assoc 1937 32 200 675 701 10.1080/01621459.1937.10503522
35. Schlotzhauer SD, Elementary Statistics Using JMP Cary, NC, USA: SAS Institute Inc 2007
36. Measuring the accuracy of diagnostic systems. Swets JA, Science 1988 240 4857 1285 1293 10.1126/science.3287615 3287615
37. Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O, J Med Chem 2005 48 7 2534 2547 10.1021/jm049092j 15801843 (Pubitemid 40520517)
38. A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Swamidass SJ, Azencott CA, Daily K, Baldi P, Bioinformatics 2010 26 10 1348 1356 10.1093/bioinformatics/btq140 20378557
39. Boosting virtual screening enrichments with data fusion: coalescing hits from two-dimensional fingerprints, shape, and docking. Sastry GM, Inakollu VS, Sherman W, J Chem Inf Model 2013 53 7 1531 1542 10.1021/ci300463g 23782297
40. A statistical framework to evaluate virtual screening. Zhao W, Hevener KE, White SW, Lee RE, Boyett JM, BMC Bioinformatics 2009 10 1 225 10.1186/1471-2105-10-225 19619306
41. Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem. Truchon J-F, Bayly CI, J Chem Inf Model 2007 47 2 488 508 10.1021/ci600426e 17288412 (Pubitemid 46615951)
42. Combination rules for group fusion in similarity-based virtual screening. Chen B, Mueller C, Willett P, Mol Info 2010 29 6-7 533 541