Journal of Computer-Aided Molecular Design

, Volume 29, Issue 9, pp 897–910 | Cite as

Tales from the war on error: the art and science of curating QSAR data

  • Marvin Waldman
  • Robert Fraczkiewicz
  • Robert D. ClarkEmail author


Curating the data underlying quantitative structure–activity relationship models is a never-ending struggle. Some curation can now be automated but much cannot, especially where data as complex as those pertaining to molecular absorption, distribution, metabolism, excretion, and toxicity are concerned (vide infra). The authors discuss some particularly challenging problem areas in terms of specific examples involving experimental context, incompleteness of data, confusion of units, problematic nomenclature, tautomerism, and misapplication of automated structure recognition tools.


Automated structure recognition Cytochrome P450 Data curation Metabolism QSAR Nomenclature Tautomerism 



The authors wish to thank Jinhua Zhang, Michael S. Lawless, Jayeeta Ghosh, and Michael Bolger for their help in ferreting out errors over the years. We also thank the Simulations Technology colleagues at Simulations Plus for their ongoing real-world testing of the models that were the ultimate product of our efforts: nothing is so effective an inducement to careful curation as knowing that the person across the hall depends on your getting it right. Thanks are also due to Ian Haworth (University of Southern California) and Terry Stouch (Science for Solutions, LLC) for the insight, inspiration, encouragement, and useful information they have provided us.


  1. 1.
    Williams AJ, Ekins S (2011) A quality alert and call for improved curation of public chemistry databases. Drug Disc Today 16(17–18):747–750. doi: 10.1016/j.drudis.2011.07.007 CrossRefGoogle Scholar
  2. 2.
    Bologa CG, Oprea TI (2012) Compound collection preparation for virtual screening. In: Larson RS (ed) Bioinformatics and drug discovery. Methods in molecular biology, 2nd edn. Humana Press, New York, pp 125–143CrossRefGoogle Scholar
  3. 3.
    Young D, Martin T, Venkatapathy R, Harten P (2008) Are the chemical structures in your QSAR correct? QSAR Comb Sci 27(11–12):1337–1345CrossRefGoogle Scholar
  4. 4.
    Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inf 29(6–7):476–488CrossRefGoogle Scholar
  5. 5.
    Williams A, Tkachenko V (2014) The Royal Society of Chemistry and the delivery of chemistry data repositories for the community. J Comput Aided Mol Des 28(10):1023–1030. doi: 10.1007/s10822-014-9784-5 CrossRefGoogle Scholar
  6. 6.
    MedChem Studio. 4.0 edn. Simulations Plus, Inc., Lancaster, CA, USAGoogle Scholar
  7. 7.
    ADMET Predictor. 7.2 edn. Simulations Plus, Inc., Lancaster, CA, USAGoogle Scholar
  8. 8.
    Fraczkiewicz R, Lobell M, Göller AH, Krenz U, Schoenneis R, Clark RD, Hillisch A (2015) Best of both worlds: combining pharma data and state of the art modeling technology to improve in silico pKa prediction. J Chem Inf Model 55(2):389–397CrossRefGoogle Scholar
  9. 9.
    World Drug Index (2008) Thomson Reuters, New YorkGoogle Scholar
  10. 10.
    Clark R, Liang W, Lee A, Lawless M, Fraczkiewicz R, Waldman M (2014) Using beta binomials to estimate classification uncertainty for ensemble models. J Cheminf 6(1):34CrossRefGoogle Scholar
  11. 11.
    Ran Y, Jain N, Yalkowsky SH (2001) Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE). J Chem Inf Comput Sci 41(5):1208–1217. doi: 10.1021/ci010287z CrossRefGoogle Scholar
  12. 12.
    Tetko IV, Sushko Y, Novotarskyi S, Patiny L, Kondratov I, Petrenko AE, Charochkina L, Asiri AM (2014) How accurately can we predict the melting points of drug-like compounds? J Chem Inf Model 54(12):3320–3329. doi: 10.1021/ci5005288 CrossRefGoogle Scholar
  13. 13.
    Lide DR (ed) (2006) CRC handbook of chemistry and physics, 86th edn. Taylor & Francis, Boca RatonGoogle Scholar
  14. 14.
    Windholz M (ed) (1983) Merck index: encyclopedia of chemicals, drugs and biologicals, 10th edn. Merck & Co Inc, RahwayGoogle Scholar
  15. 15.
    Avdeef A, Barrett DA, Shaw PN, Knaggs RD, Davis SS (1996) Octanol-, chloroform-, and propylene glycol dipelargonat-water partitioning of morphine-6-glucuronide and other related opiates. J Med Chem 39(22):4377–4381CrossRefGoogle Scholar
  16. 16.
    Clarke S, Jeffrey P (2001) Utility of metabolic stability screening: comparison of in vitro and in vivo clearance. Xenobiotica 31(8–9):591–598CrossRefGoogle Scholar
  17. 17.
    Pryde DC, Dalvie D, Hu Q, Jones P, Obach RS, Tran T-D (2010) Aldehyde oxidase: an enzyme of emerging importance in drug discovery. J Med Chem 53(24):8441–8460CrossRefGoogle Scholar
  18. 18.
    Miners JO, Knights KM, Houston JB, Mackenzie PI (2006) In vitro–in vivo correlation for drugs and other compounds eliminated by glucuronidation in humans: pitfalls and promises. Biochem Pharmacol 71(11):1531–1539CrossRefGoogle Scholar
  19. 19.
    Kaivosaari S, Finel M, Koskinen M (2011) N-glucuronidation of drugs and other xenobiotics by human and animal UDP-glucuronosyltransferases. Xenobiotica 41(8):652–669CrossRefGoogle Scholar
  20. 20.
    Bu H-Z (2006) A literature review of enzyme kinetic parameters for CYP3A4-mediated metabolic reactions of 113 drugs in human liver microsomes: structure–kinetics relationship assessment. Curr Drug Metab 7(3):231–249CrossRefGoogle Scholar
  21. 21.
    Lee CA, Kadwell SH, Kost TA, Serabjitsingh CJ (1995) CYP3A4 expressed by insect cells infected with a recombinant baculovirus containing both CYP3A4 and human NADPH-cytochrome P450 reductase is catalytically similar to human liver microsomal CYP3A4. Arch Biochem Biophys 319(1):157–167CrossRefGoogle Scholar
  22. 22.
    Venkatakrishnan K, von Moltke LL, Greenblatt DJ (1999) Nortriptyline E-10-hydroxylation in vitro is mediated by human CYP2D6 (high affinity) and CYP3A4 (low affinity): implications for interactions with enzyme-inducing drugs. J Clin Pharmacol 39(6):567–577CrossRefGoogle Scholar
  23. 23.
    Yoshii K, Kobayashi K, Tsumuji M, Tani M, Shimada N, Chiba K (2000) Identification of human cytochrome P450 isoforms involved in the 7-hydroxylation of chlorpromazine by human liver microsomes. Life Sci 67(2):175–184CrossRefGoogle Scholar
  24. 24.
    Wójcikowski J, Boksa J, Daniel WA (2010) Main contribution of the cytochrome P450 isoenzyme 1A2 (CYP1A2) to N-demethylation and 5-sulfoxidation of the phenothiazine neuroleptic chlorpromazine in human liver—a comparison with other phenothiazines. Biochem Pharmacol 80(8):1252–1259CrossRefGoogle Scholar
  25. 25.
    Morel E, Lloyd K, Dahl S (1987) Anti-apomorphine effects of phenothiazine drug metabolites. Psychopharmacol 92(1):68–72CrossRefGoogle Scholar
  26. 26.
    Mautz DS, Nelson WL, Shen DD (1995) Regioselective and stereoselective oxidation of metoprolol and bufuralol catalyzed by microsomes containing cDNA-expressed human P4502D6. Drug Metab Dispos 23(4):513–517Google Scholar
  27. 27.
    Hayhurst G, Harlow J, Chowdry J, Gross E, Hilton E, Lennard M, Tucker G, Ellis S (2001) Influence of phenylalanine-481 substitutions on the catalytic activity of cytochrome P450 2D6. Biochem J 355:373–379CrossRefGoogle Scholar
  28. 28.
    Matsunaga M, Yamazaki H, Kiyotani K, Iwano S, Saruwatari J, Nakagawa K, Soyama A, Ozawa S, Sawada J-I, Kashiyama E (2009) Two novel CYP2D6* 10 haplotypes as possible causes of a poor metabolic phenotype in Japanese. Drug Metab Dispos 37(4):699–701CrossRefGoogle Scholar
  29. 29.
    O’Reilly MC, Scott SA, Brown KA, Oguin TH, Thomas PG, Daniels JS, Morrison R, Brown HA, Lindsley CW (2013) Development of dual PLD1/2 and PLD2 selective inhibitors from a common 1,3,8-triazaspiro[4.5]decane core: discovery of ML298 and ML299 that decrease invasive migration in U87-MG glioblastoma cells. J Med Chem 56(6):2695–2699. doi: 10.1021/jm301782e CrossRefGoogle Scholar
  30. 30.
    Kiyoi T, Adam JM, Clark JK, Davies K, Easson A-M, Edwards D, Feilden H, Fields R, Francis S, Jeremiah F, McArthur D, Morrison AJ, Prosser A, Ratcliffe PD, Schulz J, Wishart G, Baker J, Campbell R, Cottney JE, Deehan M, Epemolu O, Evans L (2011) Discovery of potent and orally bioavailable heterocycle-based cannabinoid CB1 receptor agonists. Bioorg Med Chem Lett 21(6):1748–1753. doi: 10.1016/j.bmcl.2011.01.082 CrossRefGoogle Scholar
  31. 31.
    Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky YV, Ivashchenko AA, Savchuk NP, Nikolskaya T (2004) Quantitative structure–metabolism relationship modeling of metabolic N-dealkylation reaction rates. Drug Metab Dispos 32(10):1111–1120CrossRefGoogle Scholar
  32. 32.
    Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28(1):31–36. doi: 10.1021/ci00057a005 CrossRefGoogle Scholar
  33. 33.
    Weininger D, Weininger A, Weininger JL (1989) SMILES. 2. Algorithm for generation of unique SMILES notation. J Chem Inf Comput Sci 29(2):97–101. doi: 10.1021/ci00062a008 CrossRefGoogle Scholar
  34. 34.
    CAS REGISTRY—the gold standard for chemical substance information (2015) Chemical abstracts service.
  35. 35.
    Farid NA, Kurihara A, Wrighton SA (2010) Metabolism and disposition of the thienopyridine antiplatelet drugs ticlopidine, clopidogrel, and prasugrel in humans. J Clin Pharmacol 50(2):126–142CrossRefGoogle Scholar
  36. 36.
    Bartolini B, Corniello C, Sella A, Somma F, Politi V (2003) The enol tautomer of indole-3-pyruvic acid as a biological switch in stress responses. In: Allegri G, Costa CL, Ragazzi E, Steinhart H, Varesio L (eds) Developments in tryptophan and serotonin metabolism, vol 527. Advances in experimental medicine and biology. Springer, pp 601–608. doi: 10.1007/978-1-4615-0135-0_69
  37. 37.
    He M, Korzekwa KR, Jones JP, Rettie AE, Trager WF (1999) Structural forms of phenprocoumon and warfarin that are metabolized at the active site of CYP2C9. Arch Biochem Biophys 372(1):16–28. doi: 10.1006/abbi.1999.1468 CrossRefGoogle Scholar
  38. 38.
    Fernandes P, Florence AJ, Shankland K, Shankland N, Johnston A (2006) Powder study of chlorothiazide N,N-dimethylformamide solvate. Acta Crystallogr E 62(6):o2216–o2218. doi: 10.1107/S1600536806015674 CrossRefGoogle Scholar
  39. 39.
    Angyal S, Warburton W (1951) Sulphonamides. II. Structure and tautomerism of sulphapyridine, sulphathiazole, and sulphanilylbenzamidine. Aust J Chem 4(1):93–106CrossRefGoogle Scholar
  40. 40.
    Bolton EE, Wang Y, Thiessen PA, Bryant SH (2008) Chapter 12—PubChem: integrated platform of small molecules and biological activities. In: Ralph AW, David CS (eds) Annual reports in computational chemistry, vol 4. Elsevier, pp 217–241. doi: 10.1016/S1574-1400(08)00012-1
  41. 41.
    Durant G, Emmett J, Ganellin C, Miles P, Parsons M, Prain H, White G (1977) Cyanoguanidine-thiourea equivalence in the development of the histamine H2-receptor antagonist, cimetidine. J Med Chem 20(7):901–906CrossRefGoogle Scholar
  42. 42.
    Sundriyal S, Khanna S, Saha R, Bharatam PV (2008) Metformin and glitazones: Does similarity in biomolecular mechanism originate from tautomerism in these drugs? J Phys Org Chem 21(1):30–33CrossRefGoogle Scholar
  43. 43.
    Lipinski CA, Litterman NK, Southan C, Williams AJ, Clark AM, Ekins S (2015) Parallel worlds of public and commercial bioactive chemistry data. J Med Chem 58(5):2068–2076. doi: 10.1021/jm5011308 CrossRefGoogle Scholar
  44. 44.
    PubChem Substance Database (2015) National Center for Biotechnology Information. Accessed 15 Mar 2015
  45. 45.
    Hamilton JH, Hofmann S, Oganessian YT (2013) Search for superheavy nuclei. Ann Rev Nucl Part Sci 63(1):383–405. doi: 10.1146/annurev-nucl-102912-144535 CrossRefGoogle Scholar
  46. 46.
    Asimov I (1957) The marvellous properties of thiotimoline. In: Only a trillion, 1st edn. Abelard-Schuman, London, pp 178–199Google Scholar
  47. 47.
    Wikipedia (2015) ThiotimolineGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marvin Waldman
    • 1
  • Robert Fraczkiewicz
    • 1
  • Robert D. Clark
    • 1
    Email author
  1. 1.Simulations Plus, Inc.LancasterUSA

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