364
A. Zakharov and A. Lagunin
17. Gold LS, Wright C, Bernstein L, deVeciana M (1987) Reproducibility of results in “nearreplicate” carcinogenesis bioassays. J Natl Cancer Inst 78:1149–1158
18. Helma C, Kramer S, Pfahringer B, Gottmann E (2000) Data quality in predictive toxicology:
identification of chemical structures and calculation of chemical properties. Environ Health
Perspect 108:1029–1033
19. (2007) Chapter 3: Guidance on grouping of chemicals. Approaches data gap fill. Chem. Categ. OECD, pp 30–41
20. Arvidson KB (2008) FDA toxicity databases and real-time data entry. Toxicol Appl Pharmacol 233:17–19. doi:10.1016/j.taap.2007.12.033
21. Benz RD (2007) Toxicological and clinical computational analysis and the US FDA/CDER.
Expert Opin Drug Metab Toxicol 3:109–124. doi:10.1517/17425255.3.1.109
22. Richard AM, Yang C, Judson RS (2008) Toxicity data informatics: supporting a new paradigm
for toxicity prediction. Toxicol Mech Methods 18:103–118. doi:10.1080/15376510701857452
23. National Center for Toxicological Mold2. NCTR. http://www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2/default.htm. Accessed 7 May 2013
24. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structureactivity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–
266. doi:10.1080/10629360902949567
25. Dearden JC, Cronin MTD, Schultz TW, Lin DT (1995) QSAR study of the toxicity of nitrobenzenes to tetrahymena pyriformis. Quant Struct-Act Relatsh 14:427–432. doi:10.1002/
qsar.19950140503
26. Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30:138–147. doi:10.1016/j.tips.2008.12.001
27. Potemkin V, Grishina M (2008) Principles for 3D/4D QSAR classification of drugs. Drug
Discov Today 13:952–959. doi:10.1016/j.drudis.2008.07.006
28. Peristera O, Spreafico M, Smiesko M et al (2009) Mixed-model QSAR at the human mineralocorticoid receptor: predicting binding mode and affinity of anabolic steroids. Toxicol Lett
189:219–224. doi:10.1016/j.toxlet.2009.05.025
29. Rose JR (2008) Machine learning techniques in chemistry. In: Gasteiger J (ed) Handbook of
chemoinformatics. Wiley-VCH Verlag GmbH, Weinheim, Germany, pp 1082–1097
30. Martin S (2010) Machine learning-based bioinformatics algorithms: application to chemicals. Handbook of chemoinformatics algorithms. Chapman and Hall/CRC, Boca Raton,
Florida, USA, pp 383–398
31. Filimonov D, Poroikov V (2008) Probabilistic approach in activity prediction. In: Varnek A,
Tropsha A (eds) Chemoinformatics approaches virtual screen. RSC Publishing, Cambridge,
pp 182–216
32. Hewitt M, Cronin MTD, Madden JC et al (2007) Consensus QSAR models: do the benefits
outweigh the complexity? J Chem Inf Model 47:1460–1468. doi:10.1021/ci700016d
33. Lagunin A, Filimonov D, Zakharov A et al (2009) Computer-aided prediction of rodent
carcinogenicity by PASS and CISOC-PSCT. QSAR Comb Sci 28:806–810. doi:10.1002/
qsar.200860192
34. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma.
Neural Comput 4:1–58. doi:10.1162/neco.1992.4.1.1
35. Lagunin A, Zakharov A, Filimonov D, Poroikov V (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform 30:241–250. doi:10.1002/minf.201000151
36. ChemAxon Marvin Sketch. http://www.chemaxon.com/marvin/help/index.html. Accessed 8
May 2013
37. Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov Today
2:436–444. doi:10.1016/S1359-6446(97)01099-4
38. Lasser KE, Allen PD, Woolhandler SJ et al (2002) Timing of new black box warnings and
withdrawals for prescription medications. JAMA J Am Med Assoc 287:2215–2220
39. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug
Discov 3:711–715. doi:10.1038/nrd1470
A. Zakharov and A. Lagunin
17. Gold LS, Wright C, Bernstein L, deVeciana M (1987) Reproducibility of results in “nearreplicate” carcinogenesis bioassays. J Natl Cancer Inst 78:1149–1158
18. Helma C, Kramer S, Pfahringer B, Gottmann E (2000) Data quality in predictive toxicology:
identification of chemical structures and calculation of chemical properties. Environ Health
Perspect 108:1029–1033
19. (2007) Chapter 3: Guidance on grouping of chemicals. Approaches data gap fill. Chem. Categ. OECD, pp 30–41
20. Arvidson KB (2008) FDA toxicity databases and real-time data entry. Toxicol Appl Pharmacol 233:17–19. doi:10.1016/j.taap.2007.12.033
21. Benz RD (2007) Toxicological and clinical computational analysis and the US FDA/CDER.
Expert Opin Drug Metab Toxicol 3:109–124. doi:10.1517/17425255.3.1.109
22. Richard AM, Yang C, Judson RS (2008) Toxicity data informatics: supporting a new paradigm
for toxicity prediction. Toxicol Mech Methods 18:103–118. doi:10.1080/15376510701857452
23. National Center for Toxicological Mold2. NCTR. http://www.fda.gov/ScienceResearch/BioinformaticsTools/Mold2/default.htm. Accessed 7 May 2013
24. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structureactivity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–
266. doi:10.1080/10629360902949567
25. Dearden JC, Cronin MTD, Schultz TW, Lin DT (1995) QSAR study of the toxicity of nitrobenzenes to tetrahymena pyriformis. Quant Struct-Act Relatsh 14:427–432. doi:10.1002/
qsar.19950140503
26. Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30:138–147. doi:10.1016/j.tips.2008.12.001
27. Potemkin V, Grishina M (2008) Principles for 3D/4D QSAR classification of drugs. Drug
Discov Today 13:952–959. doi:10.1016/j.drudis.2008.07.006
28. Peristera O, Spreafico M, Smiesko M et al (2009) Mixed-model QSAR at the human mineralocorticoid receptor: predicting binding mode and affinity of anabolic steroids. Toxicol Lett
189:219–224. doi:10.1016/j.toxlet.2009.05.025
29. Rose JR (2008) Machine learning techniques in chemistry. In: Gasteiger J (ed) Handbook of
chemoinformatics. Wiley-VCH Verlag GmbH, Weinheim, Germany, pp 1082–1097
30. Martin S (2010) Machine learning-based bioinformatics algorithms: application to chemicals. Handbook of chemoinformatics algorithms. Chapman and Hall/CRC, Boca Raton,
Florida, USA, pp 383–398
31. Filimonov D, Poroikov V (2008) Probabilistic approach in activity prediction. In: Varnek A,
Tropsha A (eds) Chemoinformatics approaches virtual screen. RSC Publishing, Cambridge,
pp 182–216
32. Hewitt M, Cronin MTD, Madden JC et al (2007) Consensus QSAR models: do the benefits
outweigh the complexity? J Chem Inf Model 47:1460–1468. doi:10.1021/ci700016d
33. Lagunin A, Filimonov D, Zakharov A et al (2009) Computer-aided prediction of rodent
carcinogenicity by PASS and CISOC-PSCT. QSAR Comb Sci 28:806–810. doi:10.1002/
qsar.200860192
34. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma.
Neural Comput 4:1–58. doi:10.1162/neco.1992.4.1.1
35. Lagunin A, Zakharov A, Filimonov D, Poroikov V (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol Inform 30:241–250. doi:10.1002/minf.201000151
36. ChemAxon Marvin Sketch. http://www.chemaxon.com/marvin/help/index.html. Accessed 8
May 2013
37. Kennedy T (1997) Managing the drug discovery/development interface. Drug Discov Today
2:436–444. doi:10.1016/S1359-6446(97)01099-4
38. Lasser KE, Allen PD, Woolhandler SJ et al (2002) Timing of new black box warnings and
withdrawals for prescription medications. JAMA J Am Med Assoc 287:2215–2220
39. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug
Discov 3:711–715. doi:10.1038/nrd1470