In-silico prediction of sweetness using structure-activity relationship models

Food Chemistry

Volumn 253, Issue , 2018, Pages 127-131

  Goel, Anukrati ^a   Gajula, Kishore ^a   Gupta, Rakesh ^a   Rai, Beena ^a  

^a TATA RESEARCH DEVELOPMENT AND DESIGN CENTRE   (India)

Author keywords

Artificial Neural Networks (ANN); Genetic Function Approximation (GFA); Quantitative structure activity relationships (QSAR); Relative sweetness (RS)

Indexed keywords

DYES; IMPURITIES; MOLECULAR DYNAMICS; MOLECULAR GRAPHICS; MOLECULAR MODELING; MOLECULES; NEURAL NETWORKS; ORGANIC CHEMICALS; STATISTICAL TESTS; STRUCTURES (BUILT OBJECTS); SYNTHESIS (CHEMICAL); TOXIC MATERIALS;

FAST SCREENING; FIRST PRINCIPLE SIMULATION; GENETIC FUNCTION APPROXIMATIONS; MOLECULAR DOCKING; MOLECULAR DYNAMICS SIMULATIONS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSAR); RELATIVE SWEETNESS (RS); STRUCTURE ACTIVITY RELATIONSHIPS;

COMPUTATIONAL CHEMISTRY;

SUCROSE; SWEETENING AGENT;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; GENETIC PROCEDURES; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SWEETNESS; CHEMISTRY; METABOLISM; MOLECULAR DOCKING; MOLECULAR MODEL; RANDOMIZATION; TASTE;

MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RANDOM ALLOCATION; SUCROSE; SWEETENING AGENTS; TASTE;

EID: 85041512120     PISSN: 03088146     EISSN: 18737072     Source Type: Journal    
DOI: 10.1016/j.foodchem.2018.01.111     Document Type: Article

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