Combination of similarity rankings using data fusion

Journal of Chemical Information and Modeling

Volumn 53, Issue 1, 2013, Pages 1-10

  Willett, Peter ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ENGINEERING; CHEMISTRY;

MULTIPLE REFERENCES; MULTIPLE SOURCE; SIMILARITY MEASURE; SIMILARITY RANKINGS; SIMILARITY SCORES; SINGLE SOURCE; TRAINING DATA; VIRTUAL SCREENING;

DATA FUSION;

ARTIFICIAL INTELLIGENCE; CHEMICAL DATABASE; DATA BASE; DRUG SCREENING; METHODOLOGY; REVIEW; THEORETICAL MODEL;

ARTIFICIAL INTELLIGENCE; DATABASE MANAGEMENT SYSTEMS; DATABASES, CHEMICAL; DRUG EVALUATION, PRECLINICAL; MODELS, THEORETICAL;

MLCS; MLOWN;

EID: 84873681137     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci300547g     Document Type: Review

References (88)

1. Bender, A.; Glen, R. C. Molecular similarity: a key technique in molecular informatics Org. Biomol. Chem. 2004, 2, 3204-3218
2. Willett, P. Similarity methods in chemoinformatics Ann. Rev. Inf. Sci. Technol. 2009, 43, 3-71
3. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation J. Chem. Inf. Model. 2010, 50, 205-216
4. Ripphausen, P.; Nisius, B.; Bajorath, J. State-of-the-art in ligand-based virtual screening Drug Discovery Today 2011, 16, 372-376
5. Willett, P. Similarity-based data mining in files of two-dimensional chemical structures using fingerprint-based measures of molecular resemblance WIRES Data Mining Knowledge Discovery 2011, 1, 241-251
6. Johnson, M. A.; Maggiora, G. M. Concepts and Applications of Molecular Similarity; John Wiley: New York, 1990.
7. Martin, Y. C.; Kofron, J. L.; Traphagen, L. M. Do structurally similar molecules have similar biological activities? J. Med. Chem. 2002, 45, 4350-4358
8. Rogers, D.; Hahn, M. Extended-connectivity fingerprints J. Chem. Inf. Model. 2010, 50, 742-754
9. Arif, S. M.; Holliday, J. D.; Willett, P. Analysis and use of fragment occurrence data in similarity-based virtual screening J. Comput.-Aided Mol. Des. 2009, 23, 655-668
10. Stumpfe, D.; Bajorath, J. Similarity searching WIRES Comput. Mol. Sci. 2011, 1, 260-282
11. McGaughey, G. B.; Sheridan, R. P.; Bayly, C. I.; Culberson, J. C.; Kreatsoulas, C.; Lindsley, S.; Maiorov, V.; Truchon, J.-F.; Cornell, W. D. Comparison of topological, shape, and docking methods in virtual screening J. Chem. Inf. Model. 2007, 47, 1504-1519
12. Sheridan, R. P. Chemical similarity searches: when is complexity justified? Expert Opin Drug Discovery 2007, 2, 423-430
13. Bender, A.; Jenkins, J. L.; Scheiber, J.; Sukuru, S. C. K.; Glick, M.; Davies, J. W. How similar are similarity searching methods? A principal components analysis of molecular descriptor space J. Chem. Inf. Model. 2009, 49, 108-119
14. Bender, A. How similar are those molecules after all? Use two descriptors and you will have three different answers Expert Opin. Drug Discovery 2010, 5, 1141-1151
15. Sheridan, R. P.; Kearsley, S. K. Why do we need so many chemical similarity search methods? Drug Discovery Today 2002, 7, 903-911
16. Willett, P. Data fusion in ligand-based virtual screening QSAR Comb. Sci. 2006, 25, 1143-1152
17. Feher, M. Consensus scoring for protein-ligand interactions Drug Discovery Today 2006, 11, 421-428
18. Lee, H. S.; Choi, J.; Kufareva, I.; Abagyan, R.; Filikov, A.; Yang, Y.; Yoon, S. Optimization of high throughput virtual screening by combining shape-matching and docking methods J. Chem. Inf. Model. 2008, 48, 489-497
19. Tan, L.; Geppert, H.; Sisay, M. T.; Gütschow, M.; Bajorath, J. Integrating structure- and ligand-based virtual screening: comparison of individual, parallel, and fused molecular docking and similarity search calculations on multiple targets ChemMedChem 2008, 3, 1566-1571
20. Swann, S. L.; Brown, S. P.; Muchmore, S. W.; Patel, H.; Mert, P.; Locklear, J.; Hajduk, P. J. A unified, probabilistic framework for structure- and ligand-based virtual screening J. Med. Chem. 2011, 54, 1223-1232
21. Svensson, F.; Karlen, A.; Skold, C. Virtual screening data fusion using both structure- and ligand-based methods J. Chem. Inf. Model. 2012, 52, 225-232
22. Ruiz, I. L.; Urbano-Cuadrado, M.; Gómez-Nieto, M. A. Data fusion of similarity and dissimilarity measurements using Wiener-based indices for the prediction of the NPY Y5 receptor antagonist capacity of benzoxazinones J. Chem. Inf. Model. 2007, 47, 2235-2241
23. Medina-Franco, J. L.; Maggiora, G. M.; Giulianotti, M. A.; Pinilla, C.; Houghten, R. A. A similarity-based data-fusion approach to the visual characterization and comparison of compound databases Chem. Biol. Drug Des. 2007, 70, 393-412
24. Maggiora, G. M.; Shanmugasundaram, V. Molecular similarity measures Method. Mol. Biol. 2010, 672, 39-100
25. Medina-Franco, J. L.; Yongye, A. B.; Perez-Villanueva, J.; Houghten, R. A.; Martinez-Mayorga, K. Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure J. Chem. Inf. Model. 2011, 51, 2427-2439
26. Hall, D. L.; McMullen, S. A. H. Mathematical Techniques in Multisensor Data Fusion; Artech House: Norwood MA, 2004.
27. Liggins, M. E.; Hall, D. L.; Llinas, J. Handbook of Multisensor Data Fusion: Theory and Practice; CRC Press: Boca Raton FL, 2008.
28. Mitchell, H. B. Multi-Sensor Data Fusion: An Introduction; Springer: Berlin, 2007.
29. Hall, D. L.; Llinas, J. Introduction to Multisensor Data Fusion Proc. IEEE 1997, 85, 6-23
30. Klein, L. A. Sensor and Data Fusion Concepts and Applications, 2 nd ed.; SPIE Optical Engineering Press: Bellingham, 1999.
31. Dasarathy, B. V. A representative bibliography of surveys in the information fusion domain Inf. Fusion 2010, 11, 299-300
32. Whittle, M.; Gillet, V. J.; Willett, P.; Alex, A.; Loesel, J. Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: A comparison of similarity coefficients J. Chem. Inf. Comput. Sci. 2004, 44, 1840-1848
33. Kearsley, S. K.; Sallamack, S.; Fluder, E. M.; Andose, J. D.; Mosley, R. T.; Sheridan, R. P. Chemical similarity using physicochemical property descriptors J. Chem. Inf. Comput. Sci. 1996, 36, 118-127
34. Sheridan, R. P.; Miller, M. D.; Underwood, D. J.; Kearsley, S. K. Chemical similarity using geometric atom pair descriptors J. Chem. Inf. Comput. Sci. 1996, 36, 128-136
35. Willett, P.; Barnard, J. M.; Downs, G. M. Chemical similarity searching J. Chem. Inf. Comput. Sci. 1998, 38, 983-996
36. Ginn, C. M. R.; Turner, D. B.; Willett, P.; Ferguson, A. M.; Heritage, T. W. Similarity searching in files of three-dimensional chemical structures: evaluation of the EVA descriptor and combination of rankings using data fusion J. Chem. Inf. Comput. Sci. 1997, 37, 23-37
37. Ginn, C. M. R.; Willett, P.; Bradshaw, J. Combination of molecular similarity measures using data fusion Perspect. Drug Discovery Des. 2000, 20, 1-16
38. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures J. Chem. Inf. Comput. Sci. 2004, 44, 1177-1185
39. Shemetulskis, N. E.; Weininger, D.; Blankey, C. J.; Yang, J. J.; Humblet, C. Stigmata: an algorithm to determine structural commonalities in diverse datasets J. Chem. Inf. Comput. Sci. 1996, 36, 862-871
40. Singh, S. B.; Sheridan, R. P.; Fluder, E. M.; Hull, R. D. Mining the chemical quarry with joint chemical probes: an application of latent semantic structure indexing (LASSI) and TOPOSIM (Dice) to chemical database mining J. Med. Chem. 2001, 44, 1564-1575
41. Xue, L.; Stahura, F. L.; Godden, J. W.; Bajorath, J. Fingerprint scaling increases the probability of identifying molecules with similar activity in virtual screening calculations J. Chem. Inf. Comput. Sci. 2001, 41, 746-753
42. Schuffenhauer, A.; Floersheim, P.; Acklin, P.; Jacoby, E. Similarity metrics for ligands reflecting the similarity of the target proteins J. Chem. Inf. Comput. Sci. 2003, 43, 391-405
43. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. New methods for ligand-based virtual screening: use of data-fusion and machine-learning techniques to enhance the effectiveness of similarity searching J. Chem. Inf. Model. 2006, 46, 462-470
44. Yang, J.-M.; Chen, Y.-F.; Shen, T.-W.; Kristal, B. S.; Hsu, D. F. Consensus scoring criteria for improving enrichment in virtual screening J. Chem. Inf. Model. 2005, 45, 1134-1146
45. Hsu, D. F.; Taksa, I. Comparing rank and score combination methods for data fusion in information retrieval Inf. Retriev 2005, 8, 449-480
46. Goldman, B. B.; Walters, W. P. Machine learning in computational chemistry Ann. Reports Comput. Chem. 2006, 2, 127-140
47. Spärck Jones, K.; Willett, P. Readings in Information Retrieval; Morgan Kaufmann: San Francisco, CA, 1997.
48. Singhal, A. Modern information retrieval: A brief overview Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 2001, 24 (4) 35-43
49. Manning, C. D.; Raghavan, P.; Schütze, H. Introduction to Information Retrieval; Cambridge University Press: Cambridge, 2008.
50. Baeza-Yates, R.; Ribeiro-Neto, B. Modern Information Retrieval, 2 nd ed.; Addison-Wesley: Harlow, 2011.
51. Belkin, N. J.; Kantor, P.; Fox, E. A.; Shaw, J. B. Combining the evidence of multiple query representations for information retrieval Inf. Proc. Manag. 1995, 31, 431-448
52. Cormack, G. V.;; Clarke, C. L. A.;; Buettcher, S. Reciprocal rank fusion outperforms Condorcet and individual rank learning methods. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, July 19-23, 2009; pp 758-759.
53. Chen, B.; Mueller, C.; Willett, P. Combination rules for group fusion in similarity-based virtual screening Mol. Inf. 2010, 29, 533-541
54. Zhang, Q.; Muegge, I. Scaffold hopping through virtual screening using 2D and 3D similarity descriptors: ranking, voting, and consensus scoring J. Med. Chem. 2006, 49, 1536-1548
55. Cross, S.; Baroni, M.; Carosati, E.; Benedetti, P.; Clementi, S. FLAP: GRID molecular interaction fields in virtual screening. Validation using the DUD data set J. Chem. Inf. Model. 2010, 50, 1442-1450
56. Gillet, V. J.; Khatib, W.; Willett, P.; Fleming, P. J.; Green, D. V. S. Combinatorial library design using a multiobjective genetic algorithm J. Chem. Inf. Comput. Sci. 2002, 42, 375-385
57. Brown, N.; McKay, B.; Gilardoni, F.; Gasteiger, J. A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules J. Chem. Inf. Comput. Sci. 2004, 44, 1079-1087
58. Nicolaou, C. A.; Apostolakis, J.; Pattichis, C. S. De novo drug design using multiobjective evolutionary graphs J. Chem. Inf. Model. 2009, 49, 295-307
59. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures Org. Biomol. Chem. 2004, 2, 3256-3266
60. Williams, C. Reverse fingerprinting, similarity searching by group fusion and fingerprint bit importance Mol. Diversity 2006, 10, 311-332
61. Kogej, T.; Engkvist, O.; Blomberg, N.; Muresan, S. Multifingerprint based similarity searches for targeted class compound selection J. Chem. Inf. Model. 2006, 46, 1201-1213
62. Hristozov, D. P.; Oprea, T. I.; Gasteiger, J. Virtual screening applications: a study of ligand-based methods and different structure representations in four different scenarios J. Comput.-Aided Mol. Des. 2007, 21, 617-640
63. Tiikkainen, P.; Markt, P.; Wolber, G.; Kirchmair, J.; Distinto, S.; Poso, A.; Kallioniemi, O. Critical comparison of virtual screening methods against the MUV data set J. Chem. Inf. Model. 2009, 49, 2168-2178
64. Abdo, A.; Salim, N.; Ahmed, A. Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network J. Biomol. Screening 2011, 16, 1081-1088
65. Nasr, R. J.; Swamidass, S. J.; Baldi, P. F. Large scale study of multiple-molecule queries J. Cheminf. 2009, 10.1186/1758-2946-1-7
66. Hert, J.; Willett, P.; Wilton, D. J.; Acklin, P.; Azzaoui, K.; Jacoby, E.; Schuffenhauer, A. Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbour information J. Med. Chem. 2005, 48, 7049-7054
67. Raymond, J. W.; Jalaie, M.; Bradley, P. P. Conditional probability: a new fusion method for merging disparate virtual screening results J. Chem. Inf. Comput. Sci. 2004, 44, 601-609
68. Baber, J. C.; Shirley, W. A.; Gao, Y.; Feher, M. The use of consensus scoring in ligand-based virtual screening J. Chem. Inf. Model. 2006, 46, 277-288
69. Muchmore, S. W.; Debe, D. A.; Metz, J. T.; Brown, S. P.; Martin, Y. C.; Hajduk, P. J. Application of belief theory to similarity data fusion for use in analog searching and lead hopping J. Chem. Inf. Model. 2008, 48, 941-8
70. Martin, Y. C.; Muchmore, S. Beyond QSAR: lead hopping to different structures QSAR Comb. Sci. 2009, 28, 797-801
71. Tiikkainen, P.; Poso, A.; Kallioniemi, O. Comparison of structure fingerprint and molecular interaction field based methods in explaining biological similarity of small molecules in cell-based screens J. Comput.-Aided Mol. Des. 2009, 23, 227-239
72. Keiser, M. J.; Roth, B. L.; Armbruster, B. N.; Ernsberger, P.; Irwin, J. J.; Shoichet, B. K. Relating protein pharmacology by ligand chemistry Nat. Biotechnol. 2007, 25, 197-206
73. Yera, E. R.; Cleves, A. E.; Jain, A. N. Chemical structural novelty: on-targets and off-targets J. Med. Chem. 2011, 54, 6771-6785
74. Whittle, M.; Gillet, V. J.; Willett, P.; Loesel, J. Analysis of data fusion methods in virtual screening: theoretical model J. Chem. Inf. Model. 2006, 46, 2193-2205
75. Whittle, M.; Gillet, V. J.; Willett, P.; Loesel, J. Analysis of data fusion methods in virtual screening: similarity and group fusion J. Chem. Inf. Model. 2006, 46, 2206-2219
76. Holliday, J. D.; Kanoulas, E.; Malin, N.; Willett, P. Multiple search methods for similarity-based virtual screening: analysis of search overlap and precision J. Cheminf. 2011, 10.1186/1758-2946-3-29
77. Spoerri, A. Authority and ranking effects in data fusion J. Amer. Soc. Inf. Sci. Technol 2008, 59, 450-460
78. Willett, P. Textual and chemical information retrieval: different applications but similar algorithms. Inf. Res. 2000, 5 ((2); available at http://InformationR.net/ir/5-2/infres52.html.
79. Newman, M. E. J. Power laws, Pareto distributions and Zipf's law Contemp. Phys. 2005, 46, 323-351
80. Benz, R. W.; Swamidass, S. J.; Baldi, P. Discovery of power-laws in chemical space J. Chem. Inf. Model. 2008, 48, 1138-1151
81. Pao, M. L. An empirical examination of Lotka's Law J. Amer. Soc. Inf. Sci. 1986, 37, 26-33
82. Mitzenmacher, M. A brief history of generative models for power law and lognormal distributions Internet Math. 2004, 1, 226-251
83. Whittle, M.; Gillet, V. J.; Willett, P. A simulation study of the use of similarity fusion for ligand-based virtual screening. In Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques; Lodhi, H.; Yamanishi, Y., Eds.; IGI Global: Hershey PA, 2010; pp 46-59.
84. Melville, J. L.; Burke, E. K.; Hirst, J. D. Machine learning in virtual screening Comb. Chem. High Throughput Screening 2009, 12, 332-343
85. Askjaer, S.; Langgard, M. Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation J. Chem. Inf. Model. 2008, 48, 476-488
86. Wilton, D. J.; Harrison, R. F.; Willett, P.; Delaney, J.; Lawson, K.; Mullier, G. Virtual screening using binary kernel discrimination: analysis of pesticide data J. Chem. Inf. Model. 2006, 46, 471-477
87. Chen, B.; Harrison, R. F.; Papadatos, G.; Willett, P.; Wood, D. J.; Lewell, X. Q.; Greenidge, P.; Stiefl, N. Evaluation of machine-learning methods for ligand-based virtual screening J. Comput.-Aided Mol. Des. 2007, 21, 53-62
88. Bender, A. Bayesian methods in virtual screening and chemical biology Methods Mol. Biol. 2011, 672, 175-196