Progress in visual representations of chemical space

Expert Opinion on Drug Discovery

Volumn 10, Issue 9, 2015, Pages 959-973

  Osolodkin, Dmitry I ^a,b   Radchenko, Eugene V ^a,c   Orlov, Alexey A ^a,b   Voronkov, Andrey E ^a,d,e   Palyulin, Vladimir A ^a,c   Zefirov, Nikolay S ^a,c  

^a MOSCOW STATE UNIVERSITY   (Russian Federation)

^b CHUMAKOV INSTITUTE OF POLIOMYELITIS AND VIRAL ENCEPHALITIDES   (Russian Federation)

^c INSTITUTE OF PHYSIOLOGICALLY ACTIVE COMPOUNDS   (Russian Federation)

^d Digital BioPharm Ltd   (United Kingdom)

^e MOSCOW INSTITUTE OF PHYSICS AND TECHNOLOGY   (Russian Federation)

Author keywords

big data; chemical space; chemoinformatics; data mining; visualization

Indexed keywords

ATTENTION; CHEMICAL PROCEDURES; CHEMICAL SPACE; CHEMICAL STRUCTURE; COMPUTER PROGRAM; DATA BASE; DRUG ANALYSIS; EMBEDDING; GENERATIVE TOPOGRAPHIC MAPPING; HUMAN; KNOWLEDGE; MOLECULAR DYNAMICS; PRIORITY JOURNAL; REDUCTION; REVIEW; SELF ORGANIZING MAP; STOCHASTIC MODEL; STOCHASTIC NEIGHBOR EMBEDDING; STOCHASTIC PROXIMITY EMBEDDING; STRUCTURE ACTIVITY RELATION; VAN KREVELEN DIAGRAM; CHEMICAL DATABASE; CHEMICAL MODEL; DRUG DESIGN; DRUG DEVELOPMENT; MOLECULAR LIBRARY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES;

MOLECULAR LIBRARY;

DATABASES, CHEMICAL; DRUG DESIGN; DRUG DISCOVERY; HUMANS; MODELS, CHEMICAL; PRINCIPAL COMPONENT ANALYSIS; SMALL MOLECULE LIBRARIES; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84938914158     PISSN: 17460441     EISSN: 1746045X     Source Type: Journal    
DOI: 10.1517/17460441.2015.1060216     Document Type: Review

References (111)

1. Lowe D. Chemical space is big. Really big. Med Chem Commun 2015; 6: 12
2. Polishchuk PG, Madzhidov TI, Varnek A. Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 2013; 27: 675-9
3. Lusher SJ, McGuire R, van Schaik RC, et al. Data-driven medicinal chemistry in the era of big data. Drug Discov Today 2014; 19: 859-68
4. Szlezák N, Evers M, Wang J, et al. The role of big data and advanced analytics in drug discovery, development, and commercialization. Clin Pharmacol Ther 2014; 95: 492-5
5. Medina-Franco JL. Interrogating novel areas of chemical space for drug discovery using chemoinformatics. Drug Dev Res 2012; 73: 430-8
6. Oprea TI, Gottfries J. Chemography: The art of navigating in chemical space. J Comb Chem 2001; 3: 157-66
7. Rosén J, Gottfries J, Muresan S, et al. Novel chemical space exploration via natural products. J Med Chem 2009; 52: 1953-62
8. Von Lilienfeld OA. First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties. Int J Quant Chem 2013; 113: 1676-89
9. Mellinger M, Chork SCY, Dijkstra S, et al. The multivariate chemical space, and the integration of the chemical, geographical, and geophysical spaces. J Geochem Explor 1984; 21: 143-8
10. Dobson CM. Chemical space and biology. Nature 2004; 432: 824-8
11. Carbó-Dorca R. About the concept of chemical space: a concerned reflection on some trends of modern scientific thought within theoretical chemical lore. J Math Chem 2013; 51: 413-19
12. Ritchie TJ, Ertl P, Lewis R. The graphical representation of ADME-related molecule properties for medicinal chemists. Drug Discov Today 2011; 16: 65-72
13. Oellien F, Ihlenfeldt W-D, Gasteiger J. InfVis-platform-independent visual data mining of multidimensional chemical data sets. J Chem Inf Model 2005; 45: 1456-67
14. Medina-Franco JL, Martínez-Mayorga K, Giulianotti MA, et al. Visualization of the chemical space in drug discovery. Curr Comp Aided Drug Des 2008; 4: 322-33
15. Medina-Franco JL, Aguayo-Ortiz R. Progress in the visualization and mining of chemical and target spaces. Mol Inf 2013; 32: 942-53
16. Van Krevelen DW. Graphical-statistical method for the study of structure and reaction processes of coal. Fuel 1950; 26: 269-84
17. Visser SA. Application of van Krevelens graphical-statistical method for the study of aquatic humic material. Environ Sci Technol 1983; 17: 412-17
18. Kim S, Kramer RW, Hatcher PG. Graphical method for analysis of ultrahigh-resolution broadband mass spectra of natural organic matter, the van Krevelen diagram. Anal Chem 2003; 75: 5336-44
19. Wu Z, Rodgers RP, Marshall AG. Two-and three-dimensional van Krevelen diagrams: A graphical analysis complementary to the Kendrick mass plot for sorting elemental compositions of complex organic mixtures based on ultrahigh-resolution broadband Fourier transform ion cyclotron resonance mass measurements. Anal Chem 2004; 76: 2511-16
20. Nicholls A, McGaughey GB, Sheridan RP, et al. Molecular shape and medicinal chemistry: A perspective. J Med Chem 2010; 53: 3862-86
21. Ruddigkeit L, van Deursen R, Blum LC, Reymond J-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 2012; 52: 2864-75
22. Yu MJ. Druggable chemical space and enumerative combinatorics. J Cheminf 2013; 5: 19
23. Lovering F, Bikker J, Humblet C. Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 2009; 52: 6752-6
24. Johnson WT, Dress KR, Edwards M. Using the golden triangle to optimize clearance and oral absorption. Bioorg Med Chem Lett 2009; 19: 5560-4
25. Paolini GV, Shapland RHB, van Hoorn WP, et al. Global mapping of pharmacological space. Nat Biotechnol 2006; 24: 805-15
26. Lipinski CA, Lombardo F, Dominy BW, et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 1997; 23: 3-25
27. Congreve M, Carr R, Murray C, et al. A rule of three for fragment-based lead discovery? Drug Discov Today 2003; 8: 876-7
28. Jhoti H, Williams G, Rees DC, et al. The rule of three for fragment-based drug discovery: where are we now? Nat Rev Drug Discov 2013; 12: 644-5
29. Goldberg FW, Kettle JG, Kogej T, et al. Designing novel building blocks is an overlooked strategy to improve compound quality. Drug Discov Today 2015; 20: 11-17
30. Reutlinger M, Schneider G. Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. J Mol Graph Model 2012; 34: 108-17
31. Larsson J, Gottfries J, Bohlin L, Backlund A. Expanding the ChemGPS chemical space with natural products. J Nat Prod 2005; 68: 985-91
32. Larsson J, Gottfries J, Muresan S, Backlund A. ChemGPS-NP: tuned for navigation in biologically relevant chemical space. J Nat Prod 2007; 70: 789-94
33. Rosén J, Lövgren A, Kogej T, et al. ChemGPS-NPWeb: chemical space navigation online. J Comput Aided Mol Des 2009; 23: 253-9
34. Muigg P, Rosén J, Bohlin L, Backlund A. In silico comparison of marine, terrestrial and synthetic compounds using ChemGPS-NP for navigating chemical space. Phytochem Rev 2013; 12: 449-57
35. Feng Y, Campitelli M, Davis RA, Quinn RJ. Chemoinformatic analysis as a tool for prioritization of trypanocidal marine derived lead compounds. Mar Drugs 2014; 12: 1169-84
36. Lee C-L, Lin Y-T, Chang F-R, et al. Synthesis and biological evaluation of phenanthrenes as cytotoxic agents with pharmacophore modeling and ChemGPS-NP prediction as Topo II Inhibitors. PLoS ONE 2012; 7: e37897
37. Faller B, Ottaviani G, Ertl P, et al. Evolution of the physicochemical properties of marketed drugs: can history foretell the future? Drug Discov Today 2011; 16: 976-84
38. Nguyen KT, Blum LC, van Deursen R, Reymond J-L. Classification of organic molecules by molecular quantum numbers. ChemMedChem 2009; 4 (11): 1803-5
39. Ruddigkeit L, Blum LC, Reymond J-L. Visualization and virtual screening of the chemical universe database GDB-17. J Chem Inf Model 2013; 53: 56-65
40. Bürgi JJ, Awale M, Boss SD, et al. Discovery of potent positive allosteric modulators of the α3β2 nicotinic acetylcholine receptor by a chemical space walk in ChEMBL. ACS Chem Neurosci 2014; 5: 346-59
41. Ruddigkeit L, Awale M, Reymond J-L. Expanding the fragrance chemical space for virtual screening. J Cheminf 2014; 6: 27
42. Le Guilloux V, Colliandre L, Bourg S, et al. Visual characterization and diversity quantification of chemical libraries: 1. creation of delimited reference chemical subspaces. J Chem Inf Model 2011; 51: 1762-74
43. Le Guilloux V, Arrault A, Colliandre L, et al. Mining collections of compounds with screening assistant 2. J Cheminf 2012; 4: 20
44. Medina-Franco JL, Waddell J. Towards the bioassay activity landscape modeling in compound databases. J Mex Chem Soc 2012; 56: 163-8
45. Medina-Franco JL, Maggiora GM, Giulianotti MA, et al. A similarity-based data-fusion approach to the visual characterization and comparison of compound databases. Chem Biol Drug Des 2007; 70: 393-412
46. Owen JR, Nabney IT, Medina-Franco JL, López-Vallejo F. Visualization of molecular fingerprints. J Chem Inf Model 2011; 51: 1552-63
47. Medina-Franco JL, Yongye AB, Pérez-Villanueva J, et al. Multitarget structure. activity relationships characterized by activity-difference maps and consensus similarity measure. J Chem Inf Model 2011; 51: 2427-39
48. Pérez-Villanueva J, Santos R, Hernández-Campos A, et al. Structure-activity relationships of benzimidazole derivatives as antiparasitic agents: Dual activity-difference (DAD) maps. Med Chem Commun 2011; 2: 44-9
49. Lounkine E, Kutchukian P, Petrone P, et al. Chemotography for multi-target SAR analysis in the context of biological pathways. Bioorg Med Chem 2012; 20: 5416-27
50. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern 1982; 43: 59-69
51. Digles D, Ecker GF. Self-organizing maps for in silico screening and data visualization. Mol Inf 2011; 30: 838-46
52. Schmuker M, Schwarte F, Brück A, et al. SOMMER: self-organising maps for education and research. J Mol Model 2007; 13: 225-8
53. Bonachera F, Marcou G, Kireeva N, et al. Using self-organizing maps to accelerate similarity search. Bioorg Med Chem 2012; 20: 5396-409
54. Zettl H, Weggen S, Schneider P, et al. Exploring the chemical space of γ-secretase modulators. Trends Pharm Sci 2010; 31: 402-10
55. Achenbach J, Klinger F-M, Blöcher R, et al. Exploring the chemical space of multitarget ligands using aligned self-organizing maps. ACS Med Chem Lett 2013; 4: 1169-72
56. Virshup AM, Contreras-García J, Wipf P, et al. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J Amer Chem Soc 2013; 135: 7296-303
57. Reker D, Perna AM, Rodrigues T, et al. Revealing the macromolecular targets of complex natural products. Nat Chem 2014; 6: 1072-8
58. Kireeva N, Baskin II, Gaspar HA, et al. Generative Topographic Mapping (GTM): universal tool for data visualization, structure. activity modeling and dataset comparison. Mol Inf 2012; 31: 301-12
59. Bishop CM, Svensén M, Williams CKI. GTM: The generative topographic mapping. Neural Comput 1998; 10: 215-34
60. Gaspar HA, Baskin II, Marcou G, et al. Chemical data visualization and analysis with incremental generative topographic mapping: Big Data Challenge. J Chem Inf Model 2015; 55: 84-94
61. Maniyar DM, Nabney IT, Williams BS, et al. Data visualization during the early stages of drug discovery. J Chem Inf Model 2006; 46: 1806-18
62. Ovchinnikova SI, Bykov AA, Tsivadze AY, et al. Supervised extensions of chemography approaches: case studies of chemical liabilities assessment. J Cheminf 2014; 6: 20
63. Miyao T, Reker D, Schneider P, et al. Chemography of natural product space. Planta Med 2015; 81: 429-35
64. Kireeva N, Kuznetsov SL, Tsivadze AY. Toward navigating chemical space of ionic liquids: prediction of melting points using generative topographic maps. Ind Eng Chem Res 2012; 51: 14337-43
65. Gaspar HA, Marcou G, Horvath D, et al. Generative topographic mapping-based classification models and their applicability domain: application to the Biopharmaceutics Drug Disposition Classification System (BDDCS). J Chem Inf Model 2013; 53: 3318-25
66. Mishima K, Kaneko H, Funatsu K. Development of a new de novo design algorithm for exploring chemical space. Mol Inf 2014; 33: 779-89
67. Tenenbaum JB, de Silva V, Langford JC. A. Global geometric framework for nonlinear dimensionality reduction. Science 2000; 290: 2319-23
68. Kireeva NV, Ovchinnikova SI, Tetko IV, et al. Nonlinear dimensionality reduction for visualizing toxicity data: Distance-based versus topology-based approaches. ChemMedChem 2014; 9: 1047-59
69. Sammon JW. A nonlinear mapping for data structure analysis. IEEE Trans Comput 1969; 18: 401-9
70. Agrafiotis DK. Stochastic Proximity embedding. J Comput Chem 2003; 24: 1215-21
71. Agrafiotis DK, Xu H, Zhu F, et al. Stochastic proximity embedding: methods and applications. Mol Inf 2010; 29: 758-70
72. Jacoby E, Tresadern G, Bembenek S, et al. Extending kinome coverage by analysis of kinase inhibitor broad profiling data. Drug Discov Today 2015; 20 (6): 652-8
73. Hinton G, Roweis S. Stochastic neighbor embedding. Adv Neural Inform Process Syst 2002; 15: 833-40
74. Martin E, Gao E. Euclidean chemical spaces from molecular fingerprints: Hamming distance and Hempels ravens. J Comput Aided Mol Des 2015; 29: 387-95
75. Tratch SS, Zefirov NS. A. Hierarchical classification scheme for chemical reactions. J Chem Inf Comp Sci 1998; 38: 349-66
76. Tratch SS, Molchanova MS, Zefirov NS. A unified approach to characterization of molecular composition, connectivity, and configuration: symmetry, chirality, and generation problems for the corresponding combinatorial objects. MATCH Commun Math Comput Chem 2009; 61: 217-66
77. Partl C, Lex A, Streit M, et al. ConTour: Data-driven exploration of multi-relational datasets for drug discovery. IEEE Trans Vis Comp Graph 2014; 20: 1883-92
78. Palyulin VA, Radchenko EV, Zefirov NS. Molecular field topology analysis method in QSAR studies of organic compounds. J Chem Inf Comp Sci 2000; 40: 659-67
79. Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008; 4: 682-90
80. Roider HG, Pavlova N, Kirov I, et al. Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network. BMC Bioinformatics 2014; 15: 68
81. Weskamp N, Hüllermeier E, Klebe G. Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins 2009; 76: 317-30
82. Maggiora GM, Bajorath J. Chemical space networks: a powerful new paradigm for the description of chemical space. J Comput Aided Mol Des 2014; 28: 795-802
83. Zhou H, Xu P, Qu H. Visualization of bipartite relations between graphs and sets. J Vis 2015; 18: 159-72
84. Schaller RR. Moores law: Past, present and future. IEEE Spectr 1997; 34 (6): 52-9
85. Lounkine E, Wawer M, Wassermann AM, Bajorath J. SARANEA: A freely available program to mine structure. activity and structure. selectivity relationship information in compound data sets. J Chem Inf Model 2010; 50: 68-78
86. Sander T, Freyss J, von Korff M, Rufener C. DataWarrior: An open-source program for chemistry aware data visualization and analysis. J Chem Inf Model 2015; 55: 460-73
87. Thiel P, Sach-Peltason L, Ottmann C, Kohlbacher O. Blocked inverted indices for exact clustering of large chemical spaces. J Chem Inf Model 2014; 54: 2395-401
88. Tabei T, Tsuda K. SketchSort: Fast all pairs similarity search for large databases of molecular fingerprints. Mol Inf 2011; 30: 801-7
89. Wetzel S, Klein K, Renner S, et al. Interactive exploration of chemical space with Scaffold Hunter. Nat Chem Biol 2009; 5: 581-3
90. Klein K, Koch O, Kriege N, et al. Visual analysis of biological activity data with scaffold hunter. Mol Inf 2013; 32: 964-75
91. Agrafiotis DK, Wiener JJM. Scaffold Explorer: An interactive tool for organizing and mining structure. activity data spanning multiple chemotypes. J Med Chem 2010; 53: 5002-11
92. Segall MD, Champness EJ, Leeding C, et al. Breaking free from chemical spreadsheets. Drug Discov Today 2015. [Epub ahead of print]
93. StarDrop™. Optibrium Ltd. Cambridge, UK. 2014. Available from: http://optibrium.com/stardrop. [Last accessed 23 March 2015]
94. Keiser MJ, Setola V, Irwin JJ, et al. Predicting new molecular targets for known drugs. Nature 2009; 462: 175-81
95. Sushko Y, Novotarskyi S, Körner R, et al. Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process. J Cheminf 2014; 6: 48
96. Varin T, Schuffenhauer A, Ertl P, et al. Mining for bioactive scaffolds with scaffold networks: Improved compound set enrichment from primary screening data. J Chem Inf Model 2011; 51: 1528-38
97. Adams JC, Keiser MJ, Basuino L, et al. A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Comp Biol 2009; 5: e1000474
98. Langdon SR, Brown N, Blagg J. Scaffold diversity of exemplified medicinal chemistry space. J Chem Inf Model 2011; 51: 2174-85
99. Strobelt H, Bertini E, Braun J, et al. HiTSEE KNIME: a visualization tool for hit selection and analysis in high-throughput screening experiments for the KNIME platform. BMC Bioinformatics 2012; 13: S4
100. Berthold MR, Cebron N, Dill F, et al. KNIME: The Konstanz information miner. In: Preisach C, Burkhardt H, Schmidt-Thieme L, Decker R.,. editors. Data analysis, machine learning and applications. Springer; Berlin: 2008. p. 319-26
101. Rabal O, Oyarzabal J. Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space. J Chem Inf Model 2012; 52: 1086-102
102. Rabal O, Oyarzabal J. Biologically relevant chemical space navigator: from patent and structure-activity relationship analysis to library acquisition and design. J Chem Inf Model 2012; 52: 3123-37
103. Gütlein M, Karwath A, Kramer S. CheS-Mapper-chemical space mapping and visualization in 3D. J Cheminf 2012; 4: 7
104. Gütlein M, Karwath A, Kramer S. CheS-Mapper 2.0 for visual validation of (Q)SAR models. J Cheminf 2014; 6: 41
105. Awale M, van Deursen R, Reymond J-L. MQN-Mapplet: Visualization of chemical space with interactive maps of drugbank, ChEMBL, PubChem, GDB-11, and GDB-13. J Chem Inf Model 2013; 53: 509-18
106. Fiannaca A, La Rosa M, Di Fatta G, et al. The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration. J Cheminf 2014; 6: 24
107. Wolstencroft K, Haines R, Fellows D, et al. The Taverna workflow suite: designing and executing workflows of web services on the desktop, web or in the cloud. Nucleic Acids Res 2013; 41: W557-61
108. Bajorath J. Modeling of activity landscapes for drug discovery. Expert Opin Drug Discov 2012; 7: 463-73
109. Butlerow A. Einiges über die chemische Structur der Körper. Z Chem 1861; 4: 549-60
110. Schamberger J, Grimm M, Steinmeyer A, Hillisch A. Rendezvous in chemical space? comparing the small molecule compound libraries of Bayer and Schering. Drug Discov Today 2011; 16: 636-41
111. Standardizer 14.11.3.0. Chemaxon. 2014. Available from: www.chemaxon.com