Skip to main content

Advertisement

Log in

Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors

  • Research Paper
  • Published:
Pharmaceutical Research Aims and scope Submit manuscript

Abstract

Purpose

Acetylcholinesterase (AChE) is both a therapeutic target for Alzheimer’s disease and a target for organophosphorus, carbamates and chemical warfare agents. Prediction of the likelihood of compounds interacting with this enzyme is therefore important from both therapeutic and toxicological perspectives.

Materials and Methods

Support vector machine classification and regression models with molecular descriptors derived from Shape Signatures and the Molecular Operating Environment (MOE) application software were built and tested using a set of piperidine AChE inhibitors (N = 110).

Results

The combination of the alignment free Shape Signatures and 2D MOE descriptors with the Support Vector Regression method outperforms the models based solely on 2D and internal 3D (i3D) MOE descriptors, and is comparable with the best previously reported PLS model based on CoMFA molecular descriptors (\( {\text{r}}_{\text{test,SVR}}^2 = 0.48 \) vs. \( {\text{r}}_{\text{test,PLS}}^2 = 0.47 \) from Sutherland et al. J Med Chem 47:5541–5554, 2004). Support Vector Classification algorithms proved superior to a classifier based on scores from the molecular docking program GOLD, with the overall prediction accuracies being QSVC(10CV) = 74% and QSVC(LNO) = 67% vs. QGOLD = 56%.

Conclusions

These new machine learning models with combined descriptor schemes may find utility for predicting novel AChE inhibitors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.

Similar content being viewed by others

REFERENCES

  1. Moretto A. Experimental and clinical toxicology of anticholinesterase agents. Toxicol Lett. 1998;102–103:509–13.

    Article  PubMed  Google Scholar 

  2. Castro A, Martinez A. Peripheral and dual binding site acetylcholinesterase inhibitors: implications in treatment of Alzheimer’s disease. Mini Rev Med Chem. 2001;1:267–72.

    Article  PubMed  CAS  Google Scholar 

  3. Barril X, Orozco M, Luque FJ. Towards improved acetylcholinesterase inhibitors: a structural and computational approach. Mini Rev Med Chem. 2001;1:255–66.

    Article  PubMed  CAS  Google Scholar 

  4. Kaur J, Zhang MQ. Molecular modelling and QSAR of reversible acetylcholines-terase inhibitors. Curr Med Chem. 2000;7:273–94.

    PubMed  CAS  Google Scholar 

  5. Cramer RD, Patterson DE, Bunce JD. Comparative Molecular Field Analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988;110:5959–67.

    Article  CAS  Google Scholar 

  6. Tong W, Collantes ER, Chen Y, Welsh WJ. A comparative molecular field analysis study of N-benzylpiperidines as acetylcholinesterase inhibitors. J Med Chem. 1996;39:380–7.

    Article  PubMed  CAS  Google Scholar 

  7. Golbraikh A, Bernard P, Chretien JR. Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models. Eur J Med Chem. 2000;35:123–36.

    Article  PubMed  CAS  Google Scholar 

  8. El Yazal J, Rao SN, Mehl A, Slikker W Jr. Prediction of organophosphorus acetylcholinesterase inhibition using three-dimensional quantitative structure-activity relationship (3D-QSAR) methods. Toxicol Sci. 2001;63:223–32.

    Article  PubMed  CAS  Google Scholar 

  9. Sutherland JJ, O’Brien LA, Weaver DF. A comparison of methods for modeling quantitative structure-activity relationships. J Med Chem. 2004;47:5541–54.

    Article  PubMed  CAS  Google Scholar 

  10. Fernandez M, Caballero J. Ensembles of Bayesian-regularized genetic neural networks for modeling of acetylcholinesterase inhibition by huprines. Chem Biol Drug Des. 2006;68:201–12.

    Article  PubMed  CAS  Google Scholar 

  11. Akula N, Lecanu L, Greeson J, Papadopoulos V. 3D QSAR studies of AChE inhibitors based on molecular docking scores and CoMFA. Bioorg Med Chem Lett. 2006;16:6277–80.

    Article  PubMed  CAS  Google Scholar 

  12. Jung M, Tak J, Lee Y, Jung Y. Quantitative structure-activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections. Bioorg Med Chem Lett. 2007;17:1082–90.

    Article  PubMed  CAS  Google Scholar 

  13. Manchester J, Czermiński R. SAMFA: simplifying molecular descriptors for 3D-QSAR. J Chem Inf Model. 2008;48:1167–73.

    Article  PubMed  CAS  Google Scholar 

  14. Chekmarev DS, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ. Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem Res Toxicol. 2008;21:1304–14.

    Article  PubMed  CAS  Google Scholar 

  15. Kortagere S, Chekmarev D, Welsh WJ, Ekins S. New predictive models for blood-brain barrier permeability of drug-like molecules. Pharm Res. 2008;25:1836–45.

    Article  PubMed  CAS  Google Scholar 

  16. Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol. 1997;267:727–48.

    Article  PubMed  CAS  Google Scholar 

  17. Gasteiger J, Marsili M. Iterative partial equalization of orbital electronegativity–a rapid access to atomic charges. Tetrahedron 1980;36:3219–28.

    Article  CAS  Google Scholar 

  18. Zauhar RJ, Moyna G, Tian L, Li Z, Welsh WJ. Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design. J Med Chem. 2003;46:5674–90.

    Article  PubMed  CAS  Google Scholar 

  19. Nagarajan K, Zauhar R, Welsh WJ. Enrichment of ligands for the serotonin receptor using the Shape Signatures approach. J Chem Inf Model. 2005;45:49–57.

    Article  PubMed  CAS  Google Scholar 

  20. Kortagere S, Chekmarev D, Welsh WJ, Ekins S. Hybrid scoring and classification approaches to predict human pregnane X receptor activators. Pharm Res. 2009;26(4):1001-11.

    Article  PubMed  CAS  Google Scholar 

  21. Wang CY, Ai N, Arora S, Erenrich E, Nagarajan K, Zauhar R, et al. Identification of previously unrecognized antiestrogenic chemicals using a novel virtual screening approach. Chem Res Toxicol. 2006;19:1595–601.

    Article  PubMed  CAS  Google Scholar 

  22. Meek PJ, Liu Z, Tian L, Wang CY, Welsh WJ, Zauhar RJ. Shape Signatures: speeding up computer aided drug discovery. Drug Discov Today. 2006;11:895–904.

    Article  PubMed  CAS  Google Scholar 

  23. Kortagere S, Welsh WJ. Development and application of hybrid structure based method for efficient screening of ligands binding to G-protein coupled receptors. J Comput Aided Mol Des. 2006;20:789–802.

    Article  PubMed  CAS  Google Scholar 

  24. Whitley DC, Ford MG, Livingstone DJ. Unsupervised forward selection: a method for eliminating redundant variables. J Chem Inf Comput Sci. 2000;40:1160–8.

    PubMed  CAS  Google Scholar 

  25. Geladi P, Kowalski B. Partial least-squares:a tutorial. Anal Chim Acta. 1986;185:1–17.

    Article  CAS  Google Scholar 

  26. Cortes C, Vapnik V. Support vector networks. Machine Learn. 1995;20:273–93.

    Google Scholar 

  27. Vapnik V. Statistical learning theory. New York: Wiley; 1998.

    Google Scholar 

  28. Kecman V. Learning and soft computing: support vector machines, neural networks and Fuzzy logic models. Cambridge: MIT; 2001.

    Google Scholar 

  29. Ivanciuc O. Application of support vector machines in chemistry. Rev Comp Chem. 2007;23:291–400.

    Article  CAS  Google Scholar 

  30. Chen YZ, editor. Current QSAR techniques for toxicology. Hoboken: Wiley; 2007.

    Google Scholar 

  31. Xue Y, Yap CW, Sun LZ, Cao ZW, Wang JF, Chen YZ. Prediction of P-glycoprotein substrates by a support vector machine approach. J Chem Inf Comput Sci. 2004;44:1497–505.

    PubMed  CAS  Google Scholar 

  32. Leong MK. A novel approach using pharmacophore ensemble/support vector machine (PhE/SVM) for prediction of hERG liability. Chem Res Toxicol. 2007;20:217–26.

    Article  PubMed  CAS  Google Scholar 

  33. Ung CY, Li H, Yap CW, Chen YZ. In silico prediction of pregnane X receptor activators by machine learning approaches. Mol Pharmacol. 2007;71:158–68.

    Article  PubMed  CAS  Google Scholar 

  34. Song M, Breneman C, Bi J, Sukumar N, Bennett K, Cramer S, et al. Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Compu Sci. 2002;42:1347–57.

    CAS  Google Scholar 

  35. Yap CW, Li ZR, Chen YZ. Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. J Mol Graph Model. 2006;24:383–95.

    Article  PubMed  CAS  Google Scholar 

  36. Chang CC, Lin CJ. LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

  37. Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta. 1975;405:442–51.

    PubMed  CAS  Google Scholar 

  38. Kryger G, Harel M, Giles K, Toker L, Velan B, Lazar A, et al. Structures of recombinant native and E202Q mutant human acetylcholinesterase complexed with the snake-venom toxin fasciculin-II. Acta Crystallogr Sect D. 2000;56:1385–94.

    Article  CAS  Google Scholar 

  39. Guo J, Hurley MH, Wright JB, Lushington GH. A docking score function for estimating ligand-protein interactions: application to acetylcholinesterase inhibition. J Med Chem. 2004;47:5492–500.

    Article  PubMed  CAS  Google Scholar 

  40. Ekins S, Embrechts MJ, Breneman CM, Jim K, Wery J-P. Novel applications of Kernel-partial least squares to modeling a comprehensive array of properties for drug discovery. In: Ekins S, editor. Computational toxicology: risk assessment for pharmaceutical and environmental chemicals. Hoboken: Wiley-Interscience; 2007. p. 403–32.

    Google Scholar 

  41. Todeschini R, Consonni V. Handbook of molecular descriptors. Weinheim: Wiley-VCH; 2000.

    Book  Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge all those involved in the application and development of Shape Signatures. Support for this work has been provided by the USEPA-funded Environmental Bioinformatics and Computational Toxicology Center (ebCTC), under STAR Grant number GAD R 832721-010, and by the Defense Threat Reduction Agency, under contract number HDTRA- BB07TAS020. This work was also funded by NIH -GM081394 from the National Institute of General Medical Sciences (to WJW). This work has not been reviewed by and does not represent the opinions of the funding agencies.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to William J. Welsh or Sean Ekins.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplemental Table 1

Data from Sutherland et al. (9). (DOC 36 kb)

Supplemental Table 2

SVM Classification of 110 AChE compounds from Sutherland et al. (9). The dividing boundary was set at IC50 = 100 nM resulting in 51 strong and 59 weak inhibitors. (DOC 37 kb)

Supplemental Table 3

SVM classification of 110 AChE compounds from Sutherland et al. (9). The dividing boundary was set at IC50 = 250 nM resulting in 65 strong and 45 weak inhibitors. (DOC 37 kb)

Supplemental Table 4

SVM classification of 110 AChE compounds from Sutherland et al. (9). Dividing boundary was set at IC50 = 500 nM resulting in 73 strong and 37 weak inhibitors. (DOC 37 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chekmarev, D., Kholodovych, V., Kortagere, S. et al. Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors. Pharm Res 26, 2216–2224 (2009). https://doi.org/10.1007/s11095-009-9937-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11095-009-9937-8

KEY WORDS

Navigation