An overview of molecular fingerprint similarity search in virtual screening

Expert Opinion on Drug Discovery

Volumn 11, Issue 2, 2016, Pages 137-148

  Muegge, Ingo ^a   Mukherjee, Prasenjit ^a  

^a BOEHRINGER INGELHEIM PHARMACEUTICALS INC   (United States)

Author keywords

Compound acquisition; hit expansion; pharmacophore fingerprint; predictive modeling; scaffold hopping

Indexed keywords

COLLAGENASE 3; LIGAND; DRUG;

BAYES THEOREM; BIOLOGICAL ACTIVITY; COMPARATIVE STUDY; COMPUTER MODEL; ENZYME ACTIVITY; FINGER DERMATOGLYPHICS; HIGH THROUGHPUT SCREENING; MOLECULAR FINGERPRINT; MOLECULAR SIZE; PHARMACOPHORE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RANDOM FOREST; REVIEW; SCREENING; SUPPORT VECTOR MACHINE; VIRTUAL SCREENING; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; DRUG DESIGN; HUMAN; MEDICINAL CHEMISTRY; MOLECULARLY TARGETED THERAPY; PROCEDURES; STRUCTURE ACTIVITY RELATION;

CHEMISTRY, PHARMACEUTICAL; COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; DRUG DESIGN; HUMANS; MODELS, MOLECULAR; MOLECULAR TARGETED THERAPY; PHARMACEUTICAL PREPARATIONS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84957436465     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2016.1117070     Document Type: Review

References (108)

1. Johnson MA, Maggiora G. Concepts and Applications of Molecular Similarity. Johnson MA, Maggiora G., editors. New York (NY): Wiley; 1990.
2. Bender A, Glen RC. Molecular similarity: a key technique in molecular informatics. Org Biomol Chem. 2004; 2 (22): 3204-3218.
3. Roy K, Kar S, Das RN. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. New York (NY): Academic Press; 2015.
4. Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des. 2007; 13 (34): 3494-3504.
5. Varnek A, Tropsha A. Chemoinformatics approaches to virtual screening. Varnek A, Tropsha A., editors. Cambridge: RSC Publishing; 2008.
6. Willett P. Similarity-based approaches to virtual screening. Biochem Soc Trans. 2003; 31 (Pt 3): 603-606.
7. Cereto-Massague A, Ojeda MJ, Valls C., et al. Molecular fingerprint similarity search in virtual screening. Methods. 2015; 71: 58-63.
8. Willett P. Similarity-based virtual screening using 2D fingerprints. Drug Discov Today. 2006; 11 (23-24): 1046-1053.
9. Maggiora G, Vogt M, Stumpfe D., et al. Molecular similarity in medicinal chemistry. J Med Chem. 2014; 57 (8): 3186-3204.
10. Walters WP, Stahl MT, Murcko MA. Virtual screening-an overview. Drug Discov Today. 1998; 3 (4): 160-178.
11. Muegge I, Oloff S. Virtual Screening. In: Abraham DJ, Rotella DP., editors. Burgers Medicinal Chemistry, Drug Discovery, and Development. 7th ed. Hoboken (NJ): Wiley; 2010. p. 1-46.
12. Klebe G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today. 2006; 11 (13-14): 580-594.
13. Shoichet BK. Virtual screening of chemical libraries. Nature. 2004; 432 (7019): 862-865.
14. Bajorath J. Integration of virtual and high-throughput screening. Nature Rev Drug Discov. 2002; 1: 882-894.
15. Baber JC, Shirley WA, Gao Y., et al. The use of consensus scoring in ligand-based virtual screening. J Chem Inf Model. 2006; 46 (1): 277-288.
16. Zhu T, Cao S, Su PC., et al. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem. 2013; 56 (17): 6560-6572.
17. Kar S, Roy K. How far can virtual screening take us in drug discovery? Expert Opin Drug Discov. 2013; 8 (3): 245-261.
18. Sheridan RP. Chemical similarity searches: when is complexity justified? Expert Opin Drug Discov. 2007; 2 (4): 423-430.
19. Matter H, Potter T. Comparing 3D Pharmacophore Triplets and 2D Fingerprints for Selecting Diverse Compound Subsets. J Chem Inf Comput Sci. 1999;39: 1211-1225.
20. Daylight fingerprints. Daylight Chemical Information Systems, Inc. Mission Viejo (CA); 2015 [cited 2015 Nov 19]. Available from: www.daylight.com.
21. Carhart RE, Smith DH, Venkataraghavan R. Atom Pairs as Molecular Features in Structure-Activity Studies: Definition and Application. J Chem Inf Comput Sci. 1985; 25: 64-73.
22. MACCS (Molecular ACCess System). 2002. MDL information Systems, San Leandro (CA).
23. Barnard JM, Downs GM, Von Scholley-Pfab A., et al. Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries. J Mol Graph Model. 2000; 18 (4-5): 452-463.
24. PubChem Substructure Fingerprints. 2015 [cited 2015 Nov 19]. Available from: ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem-fingerprints.txt.
25. Bender A, Mussa HY, Gill GS., et al. Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D). J Med Chem. 2004; 47 (26): 6569-6583.
26. Rogers D, Hahn M. Extended-connectivity fingerprints. J Chem Inf Model. 2010; 50 (5): 742-754.
27. Schneider G, Neidhart W, Giller T., et al. "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. Angew Chem Int Ed Engl. 1999; 38 (19): 2894-2896.
28. McGregor MJ, Muskal SM. Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design. J Chem Inf Comput Sci. 1999; 39: 569-574.
29. McGregor MJ, Muskal SM. Pharmacophore Fingerprinting. 2. Application to Primary Library Design. J Chem Inf Comput Sci. 2000; 40: 117-125.
30. Mason JS, Cheney DL. Library design and virtual screening using multiple 4-point pharmacophore fingerprints. Pac Symp Biocomput. 2000;5: 576-587.
31. Unity 2D fingerprints. Certara USA Inc; 2015 [cited 2015 Nov 19]. Available from: https://www.certara.com/.
32. Schwartz J, Awale M, Reymond JL. SMIfp (SMILES fingerprint) chemical space for virtual screening and visualization of large databases of organic molecules. J Chem Inf Model. 2013; 53 (8): 1979-1989.
33. Deng Z, Chuaqui C, Singh J. Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem. 2004; 47 (2): 337-344.
34. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc.,. 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2015. 2015.
35. Rarey M, Dixon JS. Feature Trees: A new molecular similarity measure based on tree matching. J Comput Aided Mol Des. 1998; 12: 471-490.
36. Lesnik S, Stular T, Brus B., et al. LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors. J Chem Inf Model. 2015; 55 (8): 1521-1528.
37. McGaughey GB, Sheridan RP, Bayly CI., et al. Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model. 2007; 47 (4): 1504-1519.
38. Muegge I. Synergies of virtual screening approaches. Mini Rev Med Chem. 2008; 8 (9): 927-933.
39. Sheridan RP, Kearsley SK. Why do we need so many chemical similarity search methods? Drug Discov Today. 2002; 7 (17): 903-911.
40. 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 (5): 1536-1548.
41. Awale M, Reymond JL. Atom pair 2D-fingerprints perceive 3D-molecular shape and pharmacophores for very fast virtual screening of ZINC and GDB-17. J Chem Inf Model. 2014; 54 (7): 1892-1907.
42. Huang N, Shoichet BK, Irwin JJ. Benchmarking Sets for Molecular Docking. J Med Chem. 2006; 49: 6789-6801.
43. Mysinger MM, Carchia M, Irwin JJ., et al. Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem. 2012; 55 (14): 6582-6594.
44. Awale M, Jin X, Reymond JL. Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints. J Cheminform. 2015; 7: 3.
45. Horvath D, Marcou G, Varnek A. Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors. J Chem Inf Model. 2013; 53 (7): 1543-1562.
46. Patterson DE, Cramer RD, Ferguson AM., et al. Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. J Med Chem. 1996; 39 (16): 3049-3059.
47. Papadatos G, Cooper AW, Kadirkamanathan V., et al. Analysis of neighborhood behavior in lead optimization and array design. J Chem Inf Model. 2009; 49 (2): 195-208.
48. Brown RD, Martin YC. Use of Structure-Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection. J Chem Inf Comput Sci. 1996; 36: 572-584.
49. Brown RD, Martin YC. The information content of 2D and 3D structural descriptors relevant to ligand-receptor binding. J Chem Inf Comput Sci. 1997; 37: 1-9.
50. Martin YC, Kofron JL, Traphagen LM. Do structurally similar molecules have similar biological activity? J Med Chem. 2002; 45 (19): 4350-4358.
51. Taylor SJ, Abeywardane A, Liang S., et al. Fragment-based discovery of indole inhibitors of matrix metalloproteinase-13. J Med Chem. 2011; 54 (23): 8174-8187.
52. Vogt M, Stumpfe D, Geppert H., et al. Scaffold hopping using two-dimensional fingerprints: true potential, black magic, or a hopeless endeavor? Guidelines for virtual screening. J Med Chem. 2010; 53 (15): 5707-5715.
53. Sun H. Pharmacophore-based virtual screening. Curr Med Chem. 2008; 15 (10): 1018-1024.
54. Muegge I, Enyedy IJ. Virtual screening for kinase targets. Curr Med Chem. 2004; 11 (6): 693-707.
55. Muegge I, Oloff S. Advances in virtual screening. Drug Discov Today Technol. 2006; 3: 405-411.
56. Muegge I, Zhang Q. 3D virtual screening of large combinatorial spaces. Methods. 2015; 71: 14-20.
57. Hessler G, Zimmermann M, Matter H., et al. Multiple-ligand-based virtual screening: methods and applications of the MTree approach. J Med Chem. 2005; 48 (21): 6575-6584.
58. Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011; 32 (7): 1466-1474.
59. Klekota J, Roth FP. Chemical substructures that enrich for biological activity. Bioinformatics. 2008; 24 (21): 2518-2525.
60. Steinbeck C, Han Y, Kuhn S., et al. The Chemistry Development Kit (CDK): an open-source Java library for Chemo-and Bioinformatics. J Chem Inf Comput Sci. 2003; 43 (2): 493-500.
61. Ewing T, Baber JC, Feher M. Novel 2D fingerprints for ligand-based virtual screening. J Chem Inf Model. 2006; 46 (6): 2423-2431.
62. Smusz S, Kurczab R, Satala G., et al. Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands. Bioorg Med Chem Lett. 2015; 25 (9): 1827-1830.
63. Hamza A, Wei NN, Zhan CG. Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model. 2012; 52 (4): 963-974.
64. Hamza A, Wagner JM, Evans TJ., et al. Novel mycosin protease MycP(1) inhibitors identified by virtual screening and 4D fingerprints. J Chem Inf Model. 2014; 54 (4): 1166-1173.
65. JChem version 5.3.1, ChemAxon. 2015 [cited 2015 Nov 19]. Available from: http://www.chemaxon.com.
66. Gabrielsen M, Kurczab R, Siwek A., et al. Identification of novel serotonin transporter compounds by virtual screening. J Chem Inf Model. 2014; 54 (3): 933-943.
67. Marcou G, Rognan D. Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model. 2007; 47 (1): 195-207.
68. Irwin JJ, Shoichet BK. ZINC-a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005; 45 (1): 177-182.
69. Jansen C, Wang H, Kooistra AJ., et al. Discovery of novel Trypanosoma brucei phosphodiesterase B1 inhibitors by virtual screening against the unliganded TbrPDEB1 crystal structure. J Med Chem. 2013; 56 (5): 2087-2096.
70. Tsigelny IF, Kouznetsova VL, Biswas N., et al. Development of a pharmacophore model for the catecholamine release-inhibitory peptide catestatin: virtual screening and functional testing identify novel small molecule therapeutics of hypertension. Bioorg Med Chem. 2013; 21 (18): 5855-5869.
71. Distinto S, Esposito F, Kirchmair J., et al. Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint-and pharmacophore-based virtual screening approach. Eur J Med Chem. 2012; 50: 216-229.
72. Hert J, Willett P, Wilton DJ., et al. Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. Org Biomol Chem. 2004; 2 (22): 3256-3266.
73. Hert J, Willett P, Wilton DJ., et al. Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Comput Sci. 2004; 44 (3): 1177-1185.
74. Hert J, Willett P, Wilton DJ., et al. Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. J Med Chem. 2005; 48 (22): 7049-7054.
75. Hert J, Willett P, Wilton DJ., et al. New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching. J Chem Inf Model. 2006; 46 (2): 462-470.
76. Heikamp K, Bajorath J. How do 2D fingerprints detect structurally diverse active compounds? Revealing compound subset-specific fingerprint features through systematic selection. J Chem Inf Model. 2011; 51 (9): 2254-2265.
77. Bender A, Jenkins JL, Scheiber J., et al. How similar are similarity searching methods? A principal component analysis of molecular descriptor space. J Chem Inf Model. 2009; 49 (1): 108-119.
78. Duan J, Dixon SL, Lowrie JF., et al. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J Mol Graph Model. 2010; 29 (2): 157-170.
79. Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform. 2013; 5 (1): 26.
80. Whittle M, Gillet VJ, Willett P., et al. Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients. J Chem Inf Comput Sci. 2004; 44 (5): 1840-1848.
81. Whittle M, Gillet VJ, Willett P., et al. Analysis of data fusion methods in virtual screening: theoretical model. J Chem Inf Model. 2006; 46 (6): 2193-2205.
82. Duesbury E, Holliday J, Willett P. Maximum common substructure-based data fusion in similarity searching. J Chem Inf Model. 2015; 55 (2): 222-230.
83. Riniker S, Fechner N, Landrum GA. Heterogeneous classifier fusion for ligand-based virtual screening: or, how decision making by committee can be a good thing. J Chem Inf Model. 2013; 53 (11): 2829-2836.
84. Whittle M, Gillet VJ, Willett P., et al. Analysis of data fusion methods in virtual screening: similarity and group fusion. J Chem Inf Model. 2006; 46 (6): 2206-2219.
85. Muegge I. Selection Criteria for Drug-Like Compounds. Med Res Rev. 2003; 23: 302-321.
86. Wassermann AM, Geppert H, Bajorath J. Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J Chem Inf Model. 2009; 49 (3): 582-592.
87. Zang Q, Rotroff DM, Judson RS. Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods. J Chem Inf Model. 2013; 53 (12): 3244-3261.
88. Liu R, Wallqvist A. Merging applicability domains for in silico assessment of chemical mutagenicity. J Chem Inf Model. 2014; 54 (3): 793-800.
89. Zhou D, Alelyunas Y, Liu R. Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility. J Chem Inf Model. 2008; 48 (5): 981-987.
90. Clark AM, Ekins S. Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. J Chem Inf Model. 2015; 55 (6): 1246-1260.
91. Clark AM, Dole K, Coulon-Spektor A., et al. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets. J Chem Inf Model. 2015; 55 (6): 1231-1245.
92. Ajay, Walters WP, Murcko MA. Can we learn to distinguish between drug-like and nondrug-like molecules? J Med Chem. 1998; 41: 3314-3324.
93. Podlogar BL, Muegge I. "Holistic" In Silico Methods to Estimate the Synthetic and CNS Bioavailabilities of Potential Chemotherapeutic Agents. Curr Top Med Chem. 2001; 1: 257-275.
94. Balfer J, Bajorath J. Introduction of a methodology for visualization and graphical interpretation of Bayesian classification models. J Chem Inf Model. 2014; 54 (9): 2451-2468.
95. Sutherland JJ, Higgs RE, Watson I., et al. Chemical fragments as foundations for understanding target space and activity prediction. J Med Chem. 2008; 51 (9): 2689-2700.
96. Vieth M, Erickson J, Wang J., et al. Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches. J Med Chem. 2009; 52 (20): 6456-6466.
97. Bender A, Jenkins JL, Glick M., et al. "Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept? J Chem Inf Model. 2006; 46 (6): 2445-2456.
98. Olah M, Rad R, Ostopovici L., et al. WOMBAT and WOMBAT-PK: bioactivity databases for lead and drug discovery. In: Schreiber KL, Kapoor TM, Wess G., editors. Chemical Biology: From Small Molecules to Systems Biology and Drug Design. Weinheim (Germany): Wiley-VCH Verlag GmbH; 2008. p. 760-786.
99. Martin E, Mukherjee P, Sullivan D., et al. Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity. J Chem Inf Model. 2011; 51 (8): 1942-1956.
100. Mukherjee P, Martin E. Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases. J Chem Inf Model. 2012; 52 (9): 2430-2440.
101. Gaulton A, Bellis LJ, Bento AP., et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012; 40 (Database issue): D1100-D1107.
102. OBoyle NM, Banck M, James CA., et al. Open Babel: an open chemical toolbox. J Cheminform. 2011; 3: 33.
103. Steinbeck C, Hoppe C, Kuhn S., et al. Recent developments of the chemistry development kit (CDK)-an open-source java library for chemo-and bioinformatics. Curr Pharm Des. 2006; 12 (17): 2111-2120.
104. Steinbeck C, Han Y, Kuhn S., et al. The Chemistry Development Kit (CDK): an open-source Java library for Chemo-and Bioinformatics. J Chem Inf Comput Sci. 2003; 43 (2): 493-500.
105. Indigo. 2015 [cited 2015 Nov 19]. Available from: http://lifescience.opensource.epam.com/indigo/index.html
106. RDKit: 2015 [cited 2015 Nov 19]. Available from: http://www.rdkit.org.
107. Ambure A, Aher RB, Roy K. Recent Advances in the Open Access Cheminformatics Toolkits, Software Tools, Workflow Environments, and Databases. In: James KY., editors. Methods in Pharmacology and Toxicology. New York (NY): Springer Science+Business Media New York; 2014. p. 1-40.
108. Petrone PM, Wassermann AM, Lounkine E., et al. Biodiversity of small molecules-a new perspective in screening set selection. Drug Discov Today. 2013; 18 (13-14): 674-680.