Bitter sweet forest: A Random Forest based binary classifier to predict bitterness and sweetness of chemical compounds

Frontiers in Chemistry

Volumn 6, Issue APR, 2018, Pages

  Banerjee, Priyanka ^a   Preissner, Robert ^a  

^a CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

Author keywords

Bitter prediction; Fingerprints; KNIME workflow; Random Forest; Sweetness prediction; Taste prediction

Indexed keywords

EID: 85047493474     PISSN: None     EISSN: 22962646     Source Type: Journal    
DOI: 10.3389/fchem.2018.00093     Document Type: Article

References (37)

1. Ahmed, J., Preissner, S., Dunkel, M., Worth, C. L., Eckert, A., and Preissner, R. (2011). SuperSweet-a resource on natural and artificial sweetening agents. Nucleic Acids Res. 39, D377-D382. doi: 10.1093/nar/gkq917
2. Bahia, M. S., Nissim, I., and Niv, M. Y. (2017). Bitterness prediction in-Silico: a step towards better drugs. Int. J. Pharm. 536, 526-529. doi: 10.1016/j.ijpharm.2017.03.076
3. Banerjee, P., Erehman, J., Gohlke, B. O., Wilhelm, T., Preissner, R., and Dunkel, M. (2015). Super natural II-a database of natural products. Nucleic Acids Res. 43, D935-D939. doi: 10.1093/nar/gku886
4. Banerjee, P., Siramshetty, V. B., Drwal, M. N., and Preissner, R. (2016). Computational methods for prediction of in vitro effects of new chemical structures. J. Cheminform. 8:51. doi: 10.1186/s13321-016-0162-2
5. Bellisle, F. (2015). Intense sweeteners, appetite for the sweet taste, and relationship to weight management. Curr. Obes. Rep. 4, 106-110. doi: 10.1007/s13679-014-0133-8
6. Bender, A., Jenkins, J. L., Glick, M., Deng, Z., Nettles, J. H., and Davies, J. W. (2006). Bayes affinity fingerprints improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept? J. Chem. Inf. Model. 46, 2445-2456. doi: 10.1021/ci600197y
7. Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., et al. (2008). 'KNIME: the Konstanz information Miner,' in Data Analysis, Machine Learning and Applications SE-38, eds C. Preisach, H. Burkhardt, L. Schmidt-Thieme, and R. Decker (Studies in Classification, Data Analysis, and Knowledge Organization: Springer Berlin Heidelberg), 319-326
8. Birch, G. G., Dziedzicx, S. Z., Shallenberger, R. S., and Lindley, M. G. (1981). Structural functions of the sweet pharmacophore. J. Pharm. Sci. 70, 277-280. doi: 10.1002/jps.2600700313
9. Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324
10. Cardello, A. V. (1981). Comparison of taste qualities elicited by tactile, electrical, and chemical stimulation of single human taste papillae. Percept. Psychophys. 29, 163-169. doi: 10.3758/BF03207280
11. Dagan-Wiener, A., Nissim, I., Abu, N. B., Borgonovo, G., Bassoli, A., and Niv, M. Y. (2017). Bitter or Not? bitterpredict, a tool for predicting taste from chemical structure. Sci. Rep. 7:12074. doi: 10.1038/s41598-017-12359-7
12. Di Pizio, A., and Niv, M. Y. (2014). Computational studies of smell and taste receptors. Isr. J. Chem. 54, 1205-1218. doi: 10.1002/ijch.201400027
13. Drwal, M. N., Banerjee, P., Dunkel, M., Wettig, M. R., and Preissner, R. (2014). ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic Acids Res. 42, W53-W58. doi: 10.1093/nar/gku401
14. Drwal, M. N., Siramshetty, V. B., Banerjee, P., Goede, A., Preissner, R., and Dunkel, M. (2015). Molecular similarity-based predictions of the Tox21 screening outcome. Front. Environ. Sci. 3:54. doi: 10.3389/fenvs.2015.00054
15. DuBois, G. E., and Prakash, I. (2012). Non-caloric sweeteners, sweetness modulators, and sweetener enhancers. Annu. Rev. Food Sci. Technol. 3, 353-380. doi: 10.1146/annurev-food-022811-101236
16. Dwaine, R. M. (1978). Carcinogenicity of Saccharin. Environ. Health Perspect. 25, 173-200
17. Gwet, K. L. (2008). Variance estimation of nominal-scale inter-rater reliability with random selection of raters. Psychometrika 73, 407-430. doi: 10.1007/s11336-007-9054-8
18. Huang, W., Shen, Q., Su, X., Ji, M., Liu, X., Chen, Y., et al. (2016). BitterX: a tool for understanding bitter taste in humans. Sci. Rep. 6:23450. doi: 10.1038/srep23450
19. Karaman, R. (2013). Prodrugs for masking bitter taste of antibacterial drugs-a computational approach. J. Mol. Model. 19, 2399-2412. doi: 10.1007/s00894-013-1780-5
20. Krewski, D., Acosta, D., Andersen, M., Anderson, H., Bailar, J. C., Boekelheide, K., et al. (2010). Toxicity testing in the 21st century: a vision and a strategy. J. Toxicol. Environ. Health B Crit. Rev. 13, 51-138. doi: 10.1080/10937404.2010.483176
21. Kurczab, R., Smusz, S., and Bojarski, A. (2011). Evaluation of different machine learning methods for ligand-based virtual screening. J. Cheminform. 3(Suppl. 1):P41. doi: 10.1186/1758-2946-3-S1-P41
22. Lavecchia, A. (2015). Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20, 318-331. doi: 10.1016/j.drudis.2014.10.012
23. Levit, A., Nowak, S., Peters, M., Wiener, A., Meyerhof, W., Behrens, M., et al. (2014). The bitter pill: clinical drugs that activate the human bitter taste receptor TAS2R14. FASEB J. 28, 1181-1197. doi: 10.1096/fj.13-242594
24. Lindseth, G. N., Coolahan, S. E., Petros, T. V., and Lindseth, P. D. (2014). Neurobehavioral effects of aspartame consumption. Res. Nurs. Health 37, 185-193. doi: 10.1002/nur.21595
25. Livingstone, D. J. (1994). Computational techniques for the prediction of toxicity. Toxicol. In Vitro 8, 873-877. doi: 10.1016/0887-2333(94)90091-4
26. Morrison, A. B. (1979). Sugar substitutes. Can. Med. Assoc. J. 120, 633-637
27. Nickel, J., Gohlke, B. O., Erehman, J., Banerjee, P., Rong, W. W., Goede, A., et al. (2014). SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 42, W26-W31. doi: 10.1093/nar/gku477
28. Reed, D. R., and Knaapila, A. (2010). Genetics of taste and smell: poisons and pleasures. Prog. Mol. Biol. Transl. Sci. 94, 213-240. doi: 10.1016/B978-0-12-375003-7.00008-X
29. Rev, O. (2015). HHS public access 14, 871-882
30. Rojas, C., Todeschini, R., Ballabio, D., Mauri, A., Consonni, V., Tripaldi, P., et al. (2017). A QSTR-based expert system to predict sweetness of molecules. Front. Chem. 5:53. doi: 10.3389/fchem.2017.00053
31. Schiffman, S. S. (2012). Rationale for further medical and health research on high-potency sweeteners. Chem. Senses 37, 671-679. doi: 10.1093/chemse/bjs053
32. Schiffman, S. S., Booth, B. J., Losee, M. L., Pecore, S. D., and Warwick, Z. S. (1995). Bitterness of sweeteners as a function of concentration. Brain Res. Bull. 36, 505-513. doi: 10.1016/0361-9230(94)00225-P
33. Sharma, A., Amarnath, S., Thulasimani, M., and Ramaswamy, S. (2016). Artificial sweeteners as a sugar substitute: are they really safe? Indian J. Pharmacol. 48, 237. doi: 10.4103/0253-7613.182888
34. Tandel, K. R. (2011). Sugar substitutes: health controversy over perceived benefits. J. Pharm. Pharmacother. 2, 236. doi: 10.4103/0976-500X.85936
35. van Erkel, A. R., and Pattynama, P. M. (1998). Receiver Operating Characteristic (ROC) analysis: basic principles and applications in radiology. Eur. J. Radiol. 27, 88-94. doi: 10.1016/S0720-048X(97)00157-5
36. Wiener, A., Shudler, M., Levit, A., and Niv, M. Y. (2012). BitterDB: a database of bitter compounds. Nucleic Acids Res. 40, 413-419. doi: 10.1093/nar/gkr755
37. Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., et al. (2017). DrugBank 5.0: A Major Update to the Drugbank Database for 2018. Nucleic Acids Research. Edmonton, AB: Oxford University Press