Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES) to build up models for sweetness

Food Research International

Volumn 122, Issue , 2019, Pages 40-46

  Achary, P G R ^a   Toropova, A P ^b   Toropov, A A ^b  

^a SIKSHA O ANUSANDHAN DEEMED TO BE UNIVERSITY   (India)

^b MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

Author keywords

CORAL software; Index of Idelaity of correlation; Monte Carlo method; OECD principles; QSAR; Sweetness potential

Indexed keywords

GRAPHIC METHODS; MOLECULAR GRAPHICS; MOLECULES; MONTE CARLO METHODS; OPTIMAL SYSTEMS;

CORAL SOFTWARES; CORRELATION WEIGHT; OECD PRINCIPLES; OPTIMAL DESCRIPTOR; QSAR; QUANTITATIVE STRUCTURES; STATISTICAL PERFORMANCE; SWEETNESS POTENTIAL;

COMPUTATIONAL CHEMISTRY;

SUCROSE; SWEETENING AGENT;

CHEMISTRY; MOLECULAR MODEL; MONTE CARLO METHOD; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SOFTWARE;

MODELS, MOLECULAR; MONTE CARLO METHOD; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE; SUCROSE; SWEETENING AGENTS;

EID: 85063737894     PISSN: 09639969     EISSN: 18737145     Source Type: Journal    
DOI: 10.1016/j.foodres.2019.03.067     Document Type: Article

References (29)

1. Amata, E., Marrazzo, A., Dichiara, M., Modica, M.N., Salerno, L., Prezzavento, O., Pittalà V., Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors. Data in Brief 15 (2017), 281–299, 10.1016/J.DIB.2017.09.036.
2. Bassoli, A., Borgonovo, G., Morini, G., Lost and found in sweeteners: Forgotten molecules and unsolved problems in the chemistry of sweet compounds. Flavour and Fragrance Journal 26:4 (2011), 269–273, 10.1002/ffj.2064.
3. Behrens, M., Meyerhof, W., Hellfritsch, C., Hofmann, T., Sweet and umami taste: Natural products, their chemosensory targets, and beyond. Angewandte Chemie International Edition 50:10 (2011), 2220–2242, 10.1002/anie.201002094.
4. Chéron, J.-B., Casciuc, I., Golebiowski, J., Antonczak, S., Fiorucci, S., Sweetness prediction of natural compounds. Food Chemistry 221 (2017), 1421–1425, 10.1016/j.foodchem.2016.10.145.
5. Choi, J.-S., Trinh, T.X., Yoon, T.-H., Kim, J., Byun, H.-G., Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere 217 (2019), 243–249, 10.1016/J.CHEMOSPHERE.2018.11.014.
6. DuBois, G.E., Prakash, I., Non-caloric sweeteners, sweetness modulators, and sweetener enhancers. Annual Review of Food Science and Technology 3:1 (2012), 353–380, 10.1146/annurev-food-022811-101236.
7. Fioressi, S.E., Bacelo, D.E., Rojas, C., Aranda, J.F., Duchowicz, P.R., Conformation-independent quantitative structure-property relationships study on water solubility of pesticides. Ecotoxicology and Environmental Safety 171 (2019), 47–53, 10.1016/j.ecoenv.2018.12.056.
8. Ghaedi, A., Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors. Journal of Molecular Liquids 208 (2015), 269–279, 10.1016/J.MOLLIQ.2015.04.049.
9. Goel, A., Gajula, K., Gupta, R., Rai, B., In-silico prediction of sweetness using structure-activity relationship models. Food Chemistry 253 (2018), 127–131, 10.1016/J.FOODCHEM.2018.01.111.
10. Islam, M.A., Pillay, T.S., Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors. Chemometrics and Intelligent Laboratory Systems 153 (2016), 67–74, 10.1016/J.CHEMOLAB.2016.02.008.
11. Lin, L.I.-K., A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 1989, 255, 10.2307/2532051.
12. Lustig, R.H., Schmidt, L.A., Brindis, C.D., Public health: The toxic truth about sugar. Nature 482:7383 (2012), 27–29, 10.1038/482027a.
13. Ojha, P.K., Roy, K., Development of a robust and validated 2D-QSPR model for sweetness potency of diverse functional organic molecules. Food and Chemical Toxicology 112 (2018), 551–562, 10.1016/J.FCT.2017.03.043.
14. Rescifina, A., Floresta, G., Marrazzo, A., Parenti, C., Prezzavento, O., Nastasi, G., Amata, E., Development of a Sigma-2 receptor affinity filter through a Monte Carlo based QSAR analysis. European Journal of Pharmaceutical Sciences 106 (2017), 94–101, 10.1016/J.EJPS.2017.05.061.
15. Rojas, C., Ballabio, D., Consonni, V., Tripaldi, P., Mauri, A., Todeschini, R., Quantitative structure–activity relationships to predict sweet and non-sweet tastes. Theoretical Chemistry Accounts, 135(3), 2016, 66, 10.1007/s00214-016-1812-1.
16. Toropov, A.A., Achary, P.G.R., Toropova, A.P., Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles. Chemical Physics Letters 660 (2016), 107–110, 10.1016/J.CPLETT.2016.08.018.
17. Toropov, A.A., Carbó-Dorca, R., Toropova, A.P., Index of Ideality of correlation: New possibilities to validate QSAR: A case study. Structural Chemistry 29:1 (2018), 33–38, 10.1007/s11224-017-0997-9.
18. Toropov, A.A., Raška, I., Toropova, A.P., Raškova, M., Veselinović A.M., Veselinović J.B., The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models. Science of the Total Environment 659 (2019), 1387–1394, 10.1016/J.SCITOTENV.2018.12.439.
19. Toropov, A.A., Toropova, A.P., The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models?. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 819 (2017), 31–37, 10.1016/J.MRGENTOX.2017.05.008.
20. Toropov, A.A., Toropova, A.P., Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes. Chemical Physics Letters 701 (2018), 137–146, 10.1016/J.CPLETT.2018.04.012.
21. Toropova, A.P., Toropov, A.A., CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method. European Journal of Pharmaceutical Sciences 52 (2014), 21–25, 10.1016/J.EJPS.2013.10.005.
22. Toropova, A.P., Toropov, A.A., Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicology Letters 275 (2017), 57–66, 10.1016/J.TOXLET.2017.03.023.
23. Toropova, A.P., Toropov, A.A., The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?. Science of the Total Environment 586 (2017), 466–472, 10.1016/J.SCITOTENV.2017.01.198.
24. Toropova, A.P., Toropov, A.A., Use of the index of ideality of correlation to improve models of eco-toxicity. Environmental Science and Pollution Research 25:31 (2018), 31771–31775, 10.1007/s11356-018-3291-5.
25. Toropova, A.P., Toropov, A.A., QSPR and nano-QSPR: What is the difference?. Journal of Molecular Structure 1182 (2019), 141–149, 10.1016/J.MOLSTRUC.2019.01.040.
26. Toropova, A.P., Toropov, A.A., Begum, S., Achary, P.G.R., Blood brain barrier and Alzheimer's disease: Similarity and dissimilarity of molecular alerts. Current Neuropharmacology 16:6 (2018), 769–785, 10.2174/1570159X15666171016163951.
27. Velázquez-Libera, J.L., Caballero, J., Toropova, A.P., Toropov, A.A., Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity. Chemometrics and Intelligent Laboratory Systems 184 (2019), 14–21, 10.1016/J.CHEMOLAB.2018.11.008.
28. Weininger, D., SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences 28:1 (1988), 31–36, 10.1021/ci00057a005.
29. Zhong, M., Chong, Y., Nie, X., Yan, A., Yuan, Q., Prediction of sweetness by multilinear regression analysis and support vector machine. Journal of Food Science 78:9 (2013), S1445–S1450, 10.1111/1750-3841.12199.