Journal
HELIYON
Volume 9, Issue 10, Pages -Publisher
CELL PRESS
DOI: 10.1016/j.heliyon.2023.e20813
Keywords
Transfer learning; Odor thresholds; Machine learning; Artificial neural networks; Graph convolutional networks; Feedforward neural networks
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This study developed a framework based on scientific machine learning strategies and utilized transfer learning to predict odor thresholds for chemical substances. Results showed that the transfer learning-based strategy displayed better predictive performance.
Knowledge of odor thresholds is very important for the perfume industry. Due to the difficulty associated with measuring odor thresholds, empirical models capable of estimating these values can be an invaluable contribution to the field. This work developed a framework based on scientific machine learning strategies. A transfer learning-based strategy was devised, where information from a graph convolutional network predicting semantic odor descriptors was used as input data for the feedforward neural network responsible for estimating odor thresholds for chemical substances based on their molecular structures. The predictive performance of this model was compared to a benchmark odor threshold prediction model based on molecular structures that did not utilize transfer learning. Furthermore, the prediction was compared to a correlation previously proposed in the literature and a dummy regressor. Results demonstrated that the transfer learning-based strategy displayed a better predictive performance, suggesting this technique can be useful for predicting odor thresholds.
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