Modeling a crowdsourced definition of molecular complexity

Journal of Chemical Information and Modeling

Volumn 54, Issue 6, 2014, Pages 1604-1616

  Sheridan, Robert P ^a   Zorn, Nicolas ^a   Sherer, Edward C ^a   Campeau, Louis Charles ^a   Chang, Charlie ^a   Cumming, Jared ^a   Maddess, Matthew L ^a   Nantermet, Philippe G ^a   Sinz, Christopher J ^a   O'Shea, Paul D ^a  

^a MERCK RESEARCH LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL CHEMISTRY; CROWDSOURCING; MOLECULES; TOPOLOGY;

CHIRAL CENTERS; DESCRIPTORS; MOLECULAR COMPLEXITY; SELF-CONSISTENCY; WIENER INDEX;

STEREOCHEMISTRY;

CHEMICAL DATABASE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CROWDSOURCING; HUMAN; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STEREOISOMERISM;

CROWDSOURCING; DATABASES, CHEMICAL; HUMANS; MODELS, CHEMICAL; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; STEREOISOMERISM;

EID: 84903310552     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci5001778     Document Type: Article

References (34)

1. Bertz, S. H. First general index of molecular complexity J. Am. Chem. Soc. 1981, 103, 3599-3601
2. Hendrichson, J. B.; Huang, P.; Toczko, A. G. Molecular complexity: a simplified formula adapted to individual atoms J. Chem. Inf. Comput. Sci. 1987, 27, 63-67
3. Whitlock, H. W. On the structure of total synthesis of complex natural products J. Org. Chem. 1998, 63, 7982-7989
4. Barone, R.; Chanon, M. A new a simple approach to chemical complexity. Application to the synthesis of natural products J. Chem. Inf. Comput. Sci. 2001, 41, 269-272
5. Bertz, S. H. Complexity of synthetic reactions. The use of complexity indices to evaluate reactions, transforms and disconnections New J. Chem. 2003, 27, 860-869
6. Rucker, C.; Rucker, G.; Bertz, S. H. Organic synthesis-art or science J. Chem. Inf. Comput. Sci. 2004, 44, 378-386
7. Allu, T. K.; Oprea, T. I. Rapid evaluation of synthetic and molecular complexity for in silico chemistry J. Chem. Inf. Model. 2005, 45, 1237-1243
8. Schuffenhauer, A.; Brown, N.; Selzer, P.; Ertl, P.; Jacoby, E. Relationship between molecular complexity, biological activity, and structural diversity J. Chem. Inf. Model. 2006, 46, 525-535
9. 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. Jimenez-Gonzalez, C.; Ponder, C. S.; Broxterman, Q. B.; Manley, J. B. Using the right green yardstick: Why process mass intensity is used in the pharmaceutical industry to drive more sustainable processes Org. Process Res. Dev. 2011, 15, 912-917
11. Leach, A. R.; Hann, M. M. Molecular complexity and fragment-based drug discovery: ten years on Curr. Opin. Chem. Biol. 2011, 15, 489-496
12. Bonnet, P. Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists Eur. J. Med. Chem. 2012, 54, 679-689
13. Soh, S.; Wei, Y.; Kowalczyk, B.; Gothard, C. M.; Baytekin, B.; Gothard, N.; Grzybowski, B. A. Estimating chemical reactivity and cross-influence from collective chemical knowledge Chem. Sci. 2012, 3, 1497-1502
14. Von Korff, M.; Sander, T. About complexity and self-similarity of chemical structures in drug-discovery. Chaos and Complex Systems; Sttavrinides, S. G., Eds.; Springer-Verlag: Berlin, 2013; pp 301-306.
15. Kjell, D. P.; Watson, I. A.; Wolfe, C. N.; Spitler, J. T. Complexity-based metric for process mass intensity in the pharmaceutical industry Org. Process. Res. Dev. 2013, 17, 169-174
16. Hack, M. D.; Rassokhin, D. N.; Buyck, C.; Seierstad, M.; Skalkin, A.; ten Holte, P.; Jones, T. K.; Mirzadegan, T.; Agrafiotis, D. K. Library Enhancement through the Wisdom of Crowds J. Chem. Inf. Model. 2011, 51, 3275-3286
17. Kutchukian, P. S.; Vasilyeva, N. Y.; Xu, J.; Lindvall, M. K.; Dillon, M. P.; Glick, M.; Coley, J. D.; Brooijmans, N. Inside the mind of a medicinal chemist: the role of human bias in compound prioritization during drug discovery PLoS One 2012, 11, e48476
18. Peng, Z.; Gillespie, P.; Weisel, M.; So, S.-S.; So, W. V.; Kondru, R.; Narayanan, A.; Hermann, J. C. A crowd-based process and tool for HTS triage Mol. Inf. 2013, 32, 337-345
19. MDDR reference http://accelrys.com/products/databases/bioactivity/mddr. html (accessed May 2, 2014).
20. Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012, 40, D1100-D1107.
21. 200 Top Selling Drugs for 2010. www.drugs.com (accessed May 2, 2014).
22. 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
23. Pipeline Pilot reference http://accelrys.com/products/pipeline-pilot/ (accessed May 2, 2014).
24. Carhart, R. E.; Smith, D. H.; Ventkataraghavan, R. Atom pairs as molecular features in structure-activity studies: definition and application J. Chem. Inf. Comput. Sci. 1985, 25, 64-73
25. Nilakantan, R.; Bauman, N.; Dixon, J. S.; Venkataraghavan, R. Topological torsions: a new molecular descriptor for SAR applications. comparison with other descriptors J. Chem. Inf. Comput. Sci. 1987, 27, 82-85
26. Molecular Operating Environment (MOE), Version 2008, release 10, Chemical Computing Group, Montreal, Canada, 2009. http://www.chemcomp.com/ (accessed May 2, 2014).
27. Breiman, L. Random forests Machine Learning 2001, 45, 5-32
28. Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: a classification and regression tool for compound classification and QSAR modeling J. Chem. Inf. Comput. Sci. 2003, 43, 1947-1958
29. Cortes, C.; Vapnik, V. N. Support-Vector Networks Machine Learning 1995, 20, 273-297
30. LIBLINEAR - A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/ (accessed May 2, 2014).
31. Svetnik, V.; Wang, T.; Tong, C.; Liaw, A.; Sheridan, R. P.; Song, Q. Boosting: an ensemble learning tool for compound classification and QSAR modeling J. Chem. Inf. Model. 2005, 45, 786-799
32. Bruce, C. L.; Melville, J. L.; Picket, S. D.; Hirst, J. D. Contemporary QSAR classifiers compared J. Chem. Inf. Model. 2007, 47, 219-227
33. Sheridan, R. P. Time-split cross-validation as a method for estimating the goodness of prospective prediction J. Chem. Inf. Model. 2013, 53, 783-790
34. Balaban, A. T. Five new toplogical indices for the branching of tree-like graphs Theor. Chim. Acta 1979, 53, 355-375