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Pharmaceutical Research

, Volume 26, Issue 9, pp 2216–2224 | Cite as

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

Research Paper

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.

KEY WORDS

acetylcholinesterase docking machine learning molecular operating environment quantitative structure activity relationship shape signatures descriptors support vector classification support vector machine support vector regression 

Notes

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.

Supplementary material

11095_2009_9937_MOESM1_ESM.doc (36 kb)
Supplemental Table 1 Data from Sutherland et al. (9). (DOC 36 kb)
11095_2009_9937_MOESM2_ESM.doc (37 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)
11095_2009_9937_MOESM3_ESM.doc (36 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)
11095_2009_9937_MOESM4_ESM.doc (36 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)

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC)University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical SchoolPiscatawayUSA
  2. 2.Collaborations in ChemistryJenkintownUSA
  3. 3.Department of Pharmaceutical SciencesUniversity of MarylandCollege ParkUSA
  4. 4.Department of Microbiology and ImmunologyDrexel University College of MedicinePhiladelphiaUSA

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