117
Views
10
CrossRef citations to date
0
Altmetric
Articles

Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors

, , &
Pages 777-786 | Received 23 Jan 2020, Accepted 13 May 2020, Published online: 03 Jun 2020

References

  • Dos Santos Picanco LC, Ozela PF, de Fatima de Brito Brito M, et al. Alzheimer’s disease: a review from the pathophysiology to diagnosis, new perspectives for pharmacological treatment. Curr Med Chem. 2018;25(26):3141–3159.
  • Norton S, Matthews FE, Barnes DE, et al. Potential for primary prevention of Alzheimer’s disease: an analysis of population-based data. Lancet Neurol. 2014;13(8):788–794.
  • Cummings J, Lee G, Mortsdorf T, et al. Alzheimer’s disease drug pipeline: 2017. Alzheimers Dement. 2017;3(3):367–384.
  • Francis PT, Palmer AM, Snape M, et al. The cholinergic hypothesis of Alzheimer’s disease: a review of progress. J Neurol Neurosurg Psychiatry. 1999;66(2):137–147.
  • Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol. 2018;25(1):59–70.
  • Mandal S, Moudgil M, Mandal SK. Rational drug design. Eur J Pharmacol. 2009;625(1-3):90–100.
  • Neves BJ, Braga RC, Melo-Filho CC, et al. QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol. 2018;9:1275.
  • Abdel-Ilah L, Veljovic E, Pokvic G, et al. Applications of QSAR study in drug design. IJERT. 2017;6:582–587.
  • Vracko M. Advances in mathematical chemistry and applications. Amsterdam: Bentham Science, Elsevier; 2015; p. 222.
  • Danishuddin KA. Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Drug Discov Today. 2016;21(8):1291–1302.
  • Kausar S, Falcao AO. An automated framework for QSAR model building. J Cheminform. 2018;10(1):1.
  • Toropov AA, Benfenati E. SMILES in QSPR/QSAR modeling: results and perspectives. Curr Drug Discov Technol. 2007;4(2):77–116.
  • Benfenati E, Toropov AA, Toropova AP, et al. CORAL software: QSAR for Anticancer. Agents Chem Biol Drug Des. 2011;77(6):471–476.
  • Veselinovic AM, Milosavljevic JB, Toropov AA, et al. SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL. Eur J Pharm Sci. 2013;48(3):532–541.
  • Islam A, Pillay TS. Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemom Intell Lab Syst. 2016;153:67–74.
  • Kumar A, Chauhan S. Use of the Monte Carlo method for OECD principles-Guided QSAR Modeling of SIRT1 inhibitors. Arch Pharm (Weinheim). 2017;350(1):1–9.
  • Kumar A, Chauhan S. QSAR differential model for prediction of SIRT1 modulation using monte carlo method. Drug Res. 2017;67(3):156–162.
  • Kumar P, Kumar A, Sindhu J, et al. QSAR models for nitrogen containing monophosphonate and bisphosphonate derivatives as human farnesyl pyrophosphate synthase inhibitors based on monte carlo method. Drug Res. 2019;69(3):159–167.
  • Toropov AA, Toropova AP. CORALSEA; software 2019. Available from: http://insilico.eu/coral.
  • Weininger D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–36.
  • SMILES: a simplified chemical language, May 2019. Available from:https://www.daylight.com/dayhtml/doc/theory/theory.smiles.html.
  • Toropov AA, Toropova AP, Benfenati E. SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes. Eur J Med Chem. 2010;45(9):3581–3587.
  • Toropov AA, Toropova AP. The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models. Mutat Res. 2017;819:31–37.
  • Toropov AA, Raska Jr I, Torpova AP, et al. The study of the index of ideality of correlation as new criterion of predictive potential of QSPR/QSAR-models. Sci Total Enviorn. 2019;659:1387–1394.
  • Toropov AA, Toropova AP. Predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cells using index of ideality of correlation. Anticancer Res. 2018;38(11):6189–6194.
  • Toropov AA, Toropova AP. QSAR as a random event: criteria of predictive potential for a chance model. Struc Chem. 2019;30(5):1677–1683.
  • Piplani P, Danta C. Design and synthesis of newer potential 4-(N-acetylamino)phenol derived piperazine derivatives as potential cognition enhancers. Bioorg Chem. 2015;60:64–73.
  • Kulshreshtha A, Piplani P. Ameliorative effects of amide derivatives of 1,3,4-thiadiazoles on scopolamine induced cognitive dysfunction. Eur J Med Chem. 2016;122:557–573.
  • Sharma A, Piplani P. Design and synthesis of some acridine-piperazine hybrids for the improvement of cognitive dysfunction. Chem Biol Drug Des. 2017;90(5):926–935.
  • Piplani P, Sharma M, Mehta P, et al. N-(4-Hydroxyphenyl)-3,4,5-trimethoxybenzamide derivatives as potential memory enhancers: synthesis, biological evaluation and molecular simulation studies. J Biomol Struct Dyn. 2018;36(7):1867–1877.
  • Piplani P, Jain A, Devi D, et al. Design, synthesis and pharmacological evaluation of some novel indanone derivatives as acetylcholinesterase inhibitors for the management of cognitive dysfunction. Bioorg Med Chem. 2018;26(1):215–224.
  • MarvinSketch v.14.11.17.0. ChemAxon, XhemAxon Kft. Budapest, Hungary; 2014.
  • O’Boyle NM, Banck M, James CA, et al. Open Babel: an open chemical toolbox. J Cheminform. 2011;3:33.
  • Kumar A, Chauhan S. Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors. SAR QSAR Environ Res. 2017;28(3):179–197.
  • Nimbhal M, Chauhan S, Kumar P, et al. Development of prediction model for fructose-1,6-bisphosphatase inhibitors using the Monte Carlo method. SAR QSAR Environ Res. 2019;30(3):145–159.
  • Worachartcheewan A, Mandi P, Prachayasittikul V, et al. Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors. Chemometr Intell Lab. 2014;138:120–126.
  • Toropova AP, Toropov AA, Veselinovic AM, et al. Monte Carlo based quantitative structure-activity relationship models for toxicity of organic chemicals to Daphnia magna. Environ Toxicol Chem. 2016;35(11):2691–2697.
  • Toropov AA, Toropova AP, Como F, et al. Quantitative structure–activity relationship models for bee toxicity. Toxicol Environ Chem. 2017;99(7-8):1117–1128.
  • Toropova AP, Toropov AA, Rasulev BF, et al. QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES. Struct Chem. 2012;23(6):1873–1878.
  • Kumar A, Chauhan S. Use of simplified molecular input line entry system and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future Med Chem. 2018;10(13):1603–1622.
  • OECD Document. Guidance document on the validation of (quantitative) 1226 structure activity relationships (Q)SARs models. ENV/JM/MONO. 2007;2(2007):1–154.
  • Roy K, Kar S, Das RN. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. San Diego: Academic Press, Elsevier; 2015.
  • Roy PP, Leonard JT, Roy K. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometr Intell Lab. 2008;90(1):31–42.
  • Roy K. On some aspects of validation of predictive quantitative structure-activity relationship models. Expert Opin Drug Discov. 2007;2(12):1567–1577.
  • Chirico N, Gramatica P. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model. 2011;51(9):2320–2335.
  • Gramatica P. On the development and validation of QSAR models. Methods Mol Biol. 2013;930:499–526.
  • Gramatica P. Principles of QSAR models validation: internal and external. QSAR Comb Sci. 2007;26:694–701.
  • Golbraikh A, Tropsha A. Beware of q2!. J Mol Graph Model. 2002;20(4):269–276.
  • Consonni V, Ballabio D, Todeschini R. Evaluation of model predictive ability by external validation techniques. J Chemometrics. 2010;24(3-4):194–201.
  • Ojha PK, Mitra I, Das RN, et al. Further exploring rm2 metrics for validation of QSPR models. Chemometr Intell Lab. 2011;107(1):194–205.
  • Roy K, Kar S. The rm2 metrics and regression through origin approach: reliable and useful validation tools for predictive QSAR models (commentary on ‘Is regression through origin useful in external validation of QSAR models?’). Eur J Pharm Sci. 2014;62:111–114.
  • Shayanfar A, Shayanfar S. Is regression through origin useful in external validation of QSAR models? Eur J Pharm Sci. 2014;59(1):31–35.
  • Aptula N, Jeliazkova G, Schultz TW, et al. The better predictive model: high q2 for the training set or low root mean square error of prediction for the test set? QSAR Comb Sci. 2005;24(3):385–396.
  • Roy K, Das RN, Ambure P, et al. Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometr Intell Lab. 2016;152:18–33.
  • Chirico N, Gramatica P. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model. 2012;52(8):2044–2058.
  • Gramatica P, Sangion A. A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model. 2016;56(6):1127–1131.
  • Toropov AA, Toropova AP. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol Mech Methods. 2019;29(1):43–52.
  • Toropova AP, Toropov AA. The index of ideality of correlation: improvement of models for toxicity to algae. Nat Prod Res. 2019;33(15):2200–2207.
  • Toropova AP, Toropov AA. Does the index of ideality of correlation detect the better model correctly? Mol. Inform. 2019;38:1–9.
  • Toropov AA, Toropova AP. The correlation contradictions index (CCI): building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES. Sci Total Environ. 2019;681:102–109.
  • Mitra I, Saha A, Roy K. Exploring quantitative structure–activity relationship studies of antioxidant phenolic compounds obtained from traditional Chinese medicinal plants. Mol Simulat. 2010;36(13):1067–1079.
  • Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemometr Intell Lab. 2015;145:22–29.
  • Veselinović JB, Veselinovic AM, Toropova AP, et al. The Monte Carlo technique as a tool to predict LOAEL. Eur J Med Chem. 2016;116:71–75.
  • Trott O, Olson AJ. Autodock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–461.
  • Cheung J, Gary EN, Shiomi K, et al. Structures of human acetylcholinesterase bound to dihydrotanshinone I and territrem B show peripheral site flexibility. ACS Med Chem Lett. 2013;4(11):1091–1096.
  • Dassault Systèmes BIOVIA. Discovery Studio, 2019. San Diego: Dassault Systèmes; 2019.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.