PredCoffee: A binary classification approach specifically for coffee odor.
iScience
; 27(6): 110041, 2024 Jun 21.
Article
em En
| MEDLINE
| ID: mdl-38868178
ABSTRACT
Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IScience
Ano de publicação:
2024
Tipo de documento:
Article