SCUBIDOO: A Large yet Screenable and Easily Searchable Database of Computationally Created Chemical Compounds Optimized toward High Likelihood of Synthetic Tractability

Journal of Chemical Information and Modeling

Volumn 55, Issue 9, 2015, Pages 1824-1835

  Chevillard, F ^a   Kolb, P ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DIGITAL LIBRARIES; DRUG PRODUCTS; LIGANDS; MODULAR CONSTRUCTION; MOLECULES;

COMPUTATIONAL APPROACH; MODULAR BUILDINGS; OPTIMIZATION PROJECT; SEARCHABLE DATABASE; SHORT TIME FRAMES; STRUCTURE ACTIVITY RELATIONSHIPS; SYNTHETIC FEASIBILITY; VIRTUAL PRODUCTS;

DATABASE SYSTEMS;

ALGORITHM; DRUG DESIGN; DRUG DEVELOPMENT; FACTUAL DATABASE; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; DATABASES, FACTUAL; DRUG DESIGN; DRUG DISCOVERY; PRINCIPAL COMPONENT ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84942531848     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/acs.jcim.5b00203     Document Type: Article

References (51)

1. Lederberg, J. Topological mapping of organic molecules Proc. Natl. Acad. Sci. U. S. A. 1965, 53, 134-139 10.1073/pnas.53.1.134
2. Fink, T.; Reymond, J.-L. Virtual Exploration of the Chemical Universe up to 11 Atoms of C, N, O, F: Assembly of 26.4 Million Structures (110.9 Million Stereoisomers) and Analysis for New Ring Systems, Stereochemistry, Physicochemical Properties, Compound Classes, and Drug Discove J. Chem. Inf. Model. 2007, 47, 342-353 10.1021/ci600423u
3. Reutlinger, M.; Rodrigues, T.; Schneider, P.; Schneider, G. Multi-objective molecular de novo design by adaptive fragment prioritization Angew. Chem., Int. Ed. 2014, 53, 4244-4248 10.1002/anie.201310864
4. Service, R. F. The Synthesis Machine Science 2015, 347, 1190-1193 10.1126/science.347.6227.1190
5. Li, J.; Ballmer, S. G.; Gillis, E. P.; Fujii, S.; Schmidt, M. J.; Palazzolo, A. M. E.; Lehmann, J. W.; Morehouse, G. F.; Burke, M. D. Synthesis of many different types of organic small molecules using one automated process Science 2015, 347, 1221-1226 10.1126/science.aaa5414
6. Alvim-Gaston, M.; Grese, T.; Mahoui, A.; Palkowitz, A. D.; Pineiro-Nunez, M.; Watson, I. Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space Curr. Top. Med. Chem. 2014, 14, 294-303 10.2174/1568026613666131127125858
7. Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 J. Chem. Inf. Model. 2012, 52, 2864-2875 10.1021/ci300415d
8. Kolb, P.; Irwin, J. J. Docking screens: right for the right reasons? Curr. Top. Med. Chem. 2009, 9, 755-770 10.2174/156802609789207091
9. Nikitin, S.; Zaitseva, N.; Demina, O.; Solovieva, V.; Mazin, E.; Mikhalev, S.; Smolov, M.; Rubinov, A.; Vlasov, P.; Lepikhin, D.; Khachko, D.; Fokin, V.; Queen, C.; Zosimov, V. A very large diversity space of synthetically accessible compounds for use with drug design programs J. Comput.-Aided Mol. Des. 2005, 19, 47-63 10.1007/s10822-005-0097-6
10. Cramer, R. D.; Soltanshahi, F.; Jilek, R.; Campbell, B. AllChem: Generating and searching 1020 synthetically accessible structures J. Comput.-Aided Mol. Des. 2007, 21, 341-350 10.1007/s10822-006-9093-8
11. Rarey, M.; Stahl, M. Similarity searching in large combinatorial chemistry spaces J. Comput.-Aided Mol. Des. 2001, 15, 497-520 10.1023/A:1011144622059
12. Hartenfeller, M.; Eberle, M.; Meier, P.; Nieto-Oberhuber, C.; Altmann, K. H.; Schneider, G.; Jacoby, E.; Renner, S. Probing the bioactivity-relevant chemical space of robust reactions and common molecular building blocks J. Chem. Inf. Model. 2012, 52, 1167-1178 10.1021/ci200618n
13. Lessel, U.; Wellenzohn, B.; Lilienthal, M.; Claussen, H. Searching fragment spaces with feature trees J. Chem. Inf. Model. 2009, 49, 270-279 10.1021/ci800272a
14. Hu, Q.; Peng, Z.; Kostrowicki, J.; Kuki, A. Methods in Molecular Biology Chemical Library Design 2011, 685, 253-276 10.1007/978-1-60761-931-4-13
15. Peng, Z. Very large virtual compound spaces: Construction, storage and utility in drug discovery Drug Discovery Today: Technol. 2013, 10, e387-e394 10.1016/j.ddtec.2013.01.004
16. Gasteiger, J. Cheminformatics: Computing Target Complexity Nat. Chem. 2015, 7, 619-620 10.1038/nchem.2311
17. Hartenfeller, M.; Eberle, M.; Meier, P.; Nieto-Oberhuber, C.; Altmann, K.-H.; Schneider, G.; Jacoby, E.; Renner, S. A Collection of Robust Organic Synthesis Reactions for In Silico Molecule Design J. Chem. Inf. Model. 2011, 51, 3093-3098 10.1021/ci200379p
18. Roughley, S. D.; Jordan, A. M. The Medicinal Chemist's Toolbox: An Analysis of Reactions Used in the Pursuit of Drug Candidates J. Med. Chem. 2011, 54, 3451-3479 10.1021/jm200187y
19. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery MedChemComm 2011, 2, 349-355 10.1039/c1md00017a
20. ChemBridge. Building Blocks. http://www.chembridge.com/building-blocks/
21. Landrum, G. RDKit: Open-source chemoinformatics. http://www.rdkit.org.
22. Cecchini, M.; Kolb, P.; Majeux, N.; Caflisch, A. Automated docking of highly flexible ligands by genetic algorithms: a critical assessment J. Comput. Chem. 2004, 25, 412-422 10.1002/jcc.10384
23. Goldberg, F. W.; Kettle, J. G.; Kogej, T.; Perry, M. W. D.; Tomkinson, N. P. Designing novel building blocks is an overlooked strategy to improve compound quality Drug Discovery Today 2015, 20, 11-17 10.1016/j.drudis.2014.09.023
24. Baell, J. B.; Holloway, G. A. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays J. Med. Chem. 2010, 53, 2719-2740 10.1021/jm901137j
25. OMEGA, version 2.5.1.4; OpenEye Scientific Software: Santa Fe, NM; http://www.eyesopen.com.
26. Chauvet, G. Stratified balanced sampling Surv. Methodol. 2009, 35, 115-119
27. Grafstrom, A. BalancedSampling: Balanced and spatially balanced sampling. In R package version 1.4, 2014.
28. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2008.
29. Deville, J.-c.; Tillé, Y. Efficient balanced sampling: The cube method Biometrika 2004, 91, 893-912 10.1093/biomet/91.4.893
30. Hasler, C.; Tillé, Y. Fast balanced sampling for highly stratified population Comput. Stat. Data Anal. 2014, 74, 81-94 10.1016/j.csda.2013.12.005
31. DrugBank: Open Data Drug & Drug Target Database, version 4.1; http://www.drugbank.ca/.
32. Irwin, J. J.; Shoichet, B. K. ZINC-A Free Database of Commercially Available Compounds for Virtual Screening J. Chem. Inf. Model. 2005, 45, 177-182 10.1021/ci049714+
33. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank Nucleic Acids Res. 2000, 28, 235-242 10.1093/nar/28.1.235
34. Hawkins, P. C. D.; Skillman, A. G.; Warren, G. L.; Ellingson, B. A.; Stahl, M. T. Conformer generation with OMEGA: Algorithm and validation using high quality structures from the protein databank and cambridge structural database J. Chem. Inf. Model. 2010, 50, 572-584 10.1021/ci100031x
35. QUACPAC, version 1.6.3.1; OpenEye Scientific Software: Santa Fe, NM; http://www.eyesopen.com.
36. Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints J. Chem. Inf. Model. 2010, 50, 742-754 10.1021/ci100050t
37. Ertl, P.; Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions J. Cheminf. 2009, 1, 8 10.1186/1758-2946-1-8
38. Bertz, S. H. The First General Index of Molecular Complexity J. Am. Chem. Soc. 1981, 103, 3599-3601 10.1021/ja00402a071
39. Wildman, S. A.; Crippen, G. M. Prediction of Physicochemical Parameters by Atomic Contributions J. Chem. Inf. Model. 1999, 39, 868-873 10.1021/ci990307l
40. Ertl, P.; Rohde, B.; Selzer, P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties J. Med. Chem. 2000, 43, 3714-3717 10.1021/jm000942e
41. McGregor, M. J.; Pallai, P. V. Clustering of Large Database of Compounds: Using MDL keys As Structural Descriptors J. Chem. Inf. Model. 1997, 37, 443-448 10.1021/ci960151e
42. Chevillard, F.; Lagorce, D.; Reynès, C.; Villoutreix, B. O.; Vayer, P.; Miteva, M. Multimodel Protocol Based on Chemical Similarity In silico Prediction of Aqueous Solubility: A Multimodel Protocol Based on Chemical Similarity Mol. Pharmaceutics 2012, 9, 3127-3135 10.1021/mp300234q
43. Wawer, M.; Bajorath, J. Similarity-potency trees: A method to search for SAR information in compound data sets and derive SAR rules J. Chem. Inf. Model. 2010, 50, 1395-1409 10.1021/ci100197b
44. Nolen, B. J.; Tomasevic, N.; Russell, A.; Pierce, D. W.; Jia, Z.; McCormick, C. D.; Hartman, J.; Sakowicz, R.; Pollard, T. D. Characterization of two classes of small molecule inhibitors of Arp2/3 complex Nature 2009, 460, 1031-1034 10.1038/nature08231
45. Mcgann, M. FRED Pose Prediction and Virtual Screening Accuracy J. Chem. Inf. Model. 2011, 51, 578-596 10.1021/ci100436p
46. Liu, S.; Neidhardt, E.; Grossman, T.; Ocain, T.; Clardy, J. Structures of human dihydroorotate dehydrogenase in complex with antiproliferative agents Structure 2000, 8, 25-33 10.1016/S0969-2126(00)00077-0
47. 18F]Trifluoromethylating Agent for Arylboronic Acids and Aryl Iodides Chem.-Eur. J. 2014, 20, 9514-9518 10.1002/chem.201403630
48. Zanotti, G.; Cendron, L.; Folli, C.; Florio, P.; Imbimbo, B. P.; Berni, R. Structural evidence for native state stabilization of a conformationally labile amyloidogenic transthyretin variant by fibrillogenesis inhibitors FEBS Lett. 2013, 587, 2325-2331 10.1016/j.febslet.2013.06.016
49. Peretto, I. et al. Synthesis and biological activity of flurbiprofen analogues as selective inhibitors of beta-amyloid42 secretion J. Med. Chem. 2005, 48, 5705-20 10.1021/jm0502541
50. Kolb, P.; Berset Kipouros, C.; Huang, D.; Caflisch, A. Structure-based tailoring of compound libraries for high-throughput screening: Discovery of novel EphB4 kinase inhibitors Proteins: Struct., Funct., Genet. 2008, 73, 11-18 10.1002/prot.22028
51. Schneider, G. Future de novo drug design Mol. Inf. 2014, 33, 397-402 10.1002/minf.201400034