545
16 Cheminformatics: At the Crossroad of Eras
Acknowledgments The author sincerely thanks Profs. Alexandre Varnek (University of Strasbourg, France) and Alexander Tropsha (University of North Carolina at Chapel Hill, USA) for
fruitful discussions, training, support and trust. This chapter has been proofread by Dr. Laura
Widman (University of North Carolina at Chapel Hill, USA). Financial support from NSF ABI
1147145, EPA RD832720, SRC/Sematech, and UNC Junior Faculty Award is also gratefully
acknowledged.
References
1. Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J (2013) Some experiences
and opportunities for big data in translational research. Genet Med 15:802–809
2. Moore KD, Eyestone K, Coddington DC (2013) The big deal about big data. Healthc Financ
Manage 67:60–68
3. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation.
Mol Inform 29:476–488
4. Varnek A, Fourches D, Sieffert N, Solov’ev VP, Hill C, Lecomte M (2007) QSPR Modeling
of the Am III/Eu III separation factor: how far can we predict? J Solv Extr Ion Exch 25:1–26
5. Varnek A, Fourches D, Solov’ev V, Klimchuk O, Ouadi A, Billard I (2007) Successful “In
Silico” design of new efficient uranyl binders. J Solv Extr Ion Exch 25:433–462
6. Carnero A (2006) High throughput screening in drug discovery. Clin Transl Oncol 8:482–490
7. Kozikowski AP, Roth B, Tropsha A (2006) Why academic drug discovery makes sense. Science 313:1235–1236
8. Bajorath J (2013) A perspective on computational chemogenomics. Mol Inform 32:1025–
1028
9. Keiser MJ, Setola V, Irwin JJ et al (2009) Predicting new molecular targets for known drugs.
Nature 462:175–181
10. Xie X-Q (2010) Exploiting PubChem for virtual screening. Expert Opin Drug Discov
5:1205–1220
11. Williams AJ (2014) Introduction to Chemspider. http://www.chemspider.com/help-whatcan-i-do-with-chemspider.aspx. Accessed 8 Nov 2014.
12. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds
for virtual screening. J Chem Inf Model 45:177–182
13. Sipes NS, Martin MT, Kothiya P, Reif DM, Judson RS, Richard AM, Houck KA, Dix DJ,
Kavlock RJ, Knudsen TB (2013) Profiling 976 ToxCast chemicals across 331 enzymatic and
receptor signaling assays. Chem Res Toxicol 26:878–895
14. Huang R, Sakamuru S, Martin MT et al (2014) Profiling of the Tox21 10K compound library for
agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci Rep 4:5664–5673
15. Tice RR, Austin CP, Kavlock RJ, Bucher JR (2013) Improving the human hazard characterization of chemicals: a Tox21 update. Environ Health Perspect 121:756–765
16. Knudsen TB, Martin MT, Kavlock RJ, Judson RS, Dix DJ, Singh AV (2009) Profiling the
activity of environmental chemicals in prenatal developmental toxicity studies using the U.S.
EPA’s ToxRefDB. Reprod Toxicol 28:209–219
17. Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen LJ, Bork P (2012) STITCH 3:
zooming in on protein-chemical interactions. Nucleic Acids Res 40:D876–D880
18. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and
interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114
19. Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical
space based on GDB-17 data. J Comput Aided Mol Des 27:675–679
20. Ruddigkeit L, Blum LC, Reymond J-L (2013) Visualization and virtual screening of the
chemical universe database GDB-17. J Chem Inf Model 53:56–65
21. Jorgensen WL (2006) QSAR/QSPR and proprietary data. J Chem Inf Model 46:937–937
16 Cheminformatics: At the Crossroad of Eras
Acknowledgments The author sincerely thanks Profs. Alexandre Varnek (University of Strasbourg, France) and Alexander Tropsha (University of North Carolina at Chapel Hill, USA) for
fruitful discussions, training, support and trust. This chapter has been proofread by Dr. Laura
Widman (University of North Carolina at Chapel Hill, USA). Financial support from NSF ABI
1147145, EPA RD832720, SRC/Sematech, and UNC Junior Faculty Award is also gratefully
acknowledged.
References
1. Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J (2013) Some experiences
and opportunities for big data in translational research. Genet Med 15:802–809
2. Moore KD, Eyestone K, Coddington DC (2013) The big deal about big data. Healthc Financ
Manage 67:60–68
3. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation.
Mol Inform 29:476–488
4. Varnek A, Fourches D, Sieffert N, Solov’ev VP, Hill C, Lecomte M (2007) QSPR Modeling
of the Am III/Eu III separation factor: how far can we predict? J Solv Extr Ion Exch 25:1–26
5. Varnek A, Fourches D, Solov’ev V, Klimchuk O, Ouadi A, Billard I (2007) Successful “In
Silico” design of new efficient uranyl binders. J Solv Extr Ion Exch 25:433–462
6. Carnero A (2006) High throughput screening in drug discovery. Clin Transl Oncol 8:482–490
7. Kozikowski AP, Roth B, Tropsha A (2006) Why academic drug discovery makes sense. Science 313:1235–1236
8. Bajorath J (2013) A perspective on computational chemogenomics. Mol Inform 32:1025–
1028
9. Keiser MJ, Setola V, Irwin JJ et al (2009) Predicting new molecular targets for known drugs.
Nature 462:175–181
10. Xie X-Q (2010) Exploiting PubChem for virtual screening. Expert Opin Drug Discov
5:1205–1220
11. Williams AJ (2014) Introduction to Chemspider. http://www.chemspider.com/help-whatcan-i-do-with-chemspider.aspx. Accessed 8 Nov 2014.
12. Irwin JJ, Shoichet BK (2005) ZINC—a free database of commercially available compounds
for virtual screening. J Chem Inf Model 45:177–182
13. Sipes NS, Martin MT, Kothiya P, Reif DM, Judson RS, Richard AM, Houck KA, Dix DJ,
Kavlock RJ, Knudsen TB (2013) Profiling 976 ToxCast chemicals across 331 enzymatic and
receptor signaling assays. Chem Res Toxicol 26:878–895
14. Huang R, Sakamuru S, Martin MT et al (2014) Profiling of the Tox21 10K compound library for
agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci Rep 4:5664–5673
15. Tice RR, Austin CP, Kavlock RJ, Bucher JR (2013) Improving the human hazard characterization of chemicals: a Tox21 update. Environ Health Perspect 121:756–765
16. Knudsen TB, Martin MT, Kavlock RJ, Judson RS, Dix DJ, Singh AV (2009) Profiling the
activity of environmental chemicals in prenatal developmental toxicity studies using the U.S.
EPA’s ToxRefDB. Reprod Toxicol 28:209–219
17. Kuhn M, Szklarczyk D, Franceschini A, von Mering C, Jensen LJ, Bork P (2012) STITCH 3:
zooming in on protein-chemical interactions. Nucleic Acids Res 40:D876–D880
18. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and
interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114
19. Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical
space based on GDB-17 data. J Comput Aided Mol Des 27:675–679
20. Ruddigkeit L, Blum LC, Reymond J-L (2013) Visualization and virtual screening of the
chemical universe database GDB-17. J Chem Inf Model 53:56–65
21. Jorgensen WL (2006) QSAR/QSPR and proprietary data. J Chem Inf Model 46:937–937