#5797. In silico prediction of fragrance retention grades for monomer flavors using QSPR models

August 2026publication date
Proposal available till 11-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Spectroscopy;
Analytical Chemistry;
Computer Science Applications;
Software;
Process Chemistry and Technology;
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Abstract:
The fragrance retention grades (FRGs) of monomer flavors contribute significantly to the perfumer technology development. In silico prediction of FRGs of monomer flavors are required to reduce costs, time, and manual testing. Quantitative structure-property relationships (QSPR) were established employing a database of monomer flavors, including 1552 odorants and corresponding FRGs. Molecular structure physicochemical information of the odorant molecules was acquired using a molecular calculation software (Dragon 7.0). To obviate the challenge of high dimensionality, we employed five feature extractors, including principal component analysis, lasso, recursive feature elimination, autoencoder, and boruta algorithm. Moreover, three machine learning algorithms were applied and compared to develop QSPR models for the estimation of the FRGs of monomer flavors.
Keywords:
Applicability domain; In silico prediction; Machine learning; Molecular descriptors; QSPR models

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