Computer aided aroma design I–Molecular knowledge framework

Korichi, Mourad; Gerbaud, Vincent; Floquet, Pascal; Méniai, Abdeslam-Hassen; Nacef, Salif; Joulia, Xavier

doi:10.1016/j.cep.2008.02.008

Cited by 19 publications

(14 citation statements)

References 40 publications

(44 reference statements)

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“…The CAPD search can be performed in several sequential levels (Harper et al, 1999;Korichi et al, 2008). At low level, simple and/or fast-computing property prediction models are used over a large population.…”

Section: The Search Algorithmmentioning

confidence: 99%

Computer aided product design tool for sustainable product development

Heintz

Belaud

Pandya

et al. 2014

Computers & Chemical Engineering

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A computer aided product design (CAPD) tool is proposed that finds mixtures matching target properties. Genetic algorithm crossover and mutation operators are completed with insertion or deletion operators adapted for side branches. A new substitution operator is devised for cyclic molecules. The mixture fitness is evaluated by a weighted sum of property performances. Molecules are represented by molecular graphs. They are split into molecular fragments which are built from polyatomic groups. Molecules or molecular fragments can be fixed, constrained or left free for building a new molecule. Building blocks are chemical functional groups or bio-sourced synthons. A specific coding of hydrogen-suppressed atoms is devised that can be used with various property estimation models where atom connectivity information is required. Illustration is provided through three case studies to find levulinic, glycerol and bio-based derivatives as substitute for chlorinated paraffin, methyl p-coumarate ester solvent and blanket wash solvent, respectively.

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Section: The Search Algorithmmentioning

confidence: 99%

Computer aided product design tool for sustainable product development

Heintz

Belaud

Pandya

et al. 2014

Computers & Chemical Engineering

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“…Mattei et al (2014) extended the methodology to emulsion-based formulated products. Korichi et al (2008) presented a multi-level molecular knowledge framework for computer aided aroma design. Martín and Martínez (2013) presented a mathematical optimization-based methodology to simultaneously formulate a laundry detergent product and design its manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

A grand model for chemical product design

Fung

Zhang

et al. 2016

Computers & Chemical Engineering

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“…Ishikawa and Kishi studied 203 musk‐related molecules and established a relationship between the electronic parameters of the molecules and their odour intensities by performing a discriminative function analysis and multi‐linear regression analysis. Korichi et al . developed a QSPR model showing the importance of molecular descriptors to predict the odour quality of 121 molecules of balsamic odour employing artificial neural networks and discriminant analysis after reducing the number of descriptors through a variable selection approach.…”

Section: Introductionmentioning

confidence: 99%

A quantitative structure–property relationship approach to determine the essential molecular functionalities of potent odorants

Pal

Mitra

Roy

2013

Flavour & Fragrance J

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For the last 100 years, considerable effort has been oriented towards the investigation of odorous molecules from natural sources and the development of new synthetic odorants. In this search, cheminformatic tools for quantitative structure–property relationships (QSPRs) have been shown to be beneficial for the primary screening of odorant molecules. The present work attempts to identify the distinct chemical features that impart a strong smell to the molecules. Here, genetic/partial least squares (G/PLS) regression has been employed using an easily interpretable descriptor pool for generating a QSPR model to predict odour detection thresholds, using 204 diverse airborne chemicals. The model was rigorously validated using a variety of statistical parameters and different validation metrics which yielded good results. The statistical model developed provides valuable information about the contribution of structural fragments that are essential for lowering the odour threshold of the molecules, which may guide the development of new potent odorants. The interpretation of the descriptors contained in the model provides knowledge about the requirement of nucleophilicity of a molecule in relation to its binding with odorant receptors. Based on the predictive power and interpretability of the model, they might be further utilized for guiding the design and screening of new stronger odorant molecules. Copyright © 2013 John Wiley & Sons, Ltd.

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Computer aided aroma design I–Molecular knowledge framework

Cited by 19 publications

References 40 publications

Computer aided product design tool for sustainable product development

Computer aided product design tool for sustainable product development

A grand model for chemical product design

A quantitative structure–property relationship approach to determine the essential molecular functionalities of potent odorants

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