Structure–camphor odour relationships using the Generation and Selection of Pertinent Descriptors approach

Zakarya, Driss; Chastrette, M.; Tollabi, Mustapha; Fkih‐Tetouani, S.

doi:10.1016/s0169-7439(99)00003-9

Cited by 18 publications

(10 citation statements)

References 21 publications

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“… 75 kNN has been used to predict the odor characteristics of compounds. 76 , 77 Studies using kNN to investigate physicochemical properties have considered melting point, 52 , 69 boiling point, 78 logP, 52 , 78 aqueous solubility, 52 , 79 and the analysis of mixtures. 80 …”

Section: Machine-learning Methodsmentioning

confidence: 99%

“…Studies of toxicological and pharmacological relevance have looked at drug clearance, mutagenic potency, and percutaneous drug absorption . kNN has been used to predict the odor characteristics of compounds . Studies using kNN to investigate physicochemical properties have considered melting point, boiling point, logP, aqueous solubility, and the analysis of mixtures …”

Section: Machine‐learning Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning methods in chemoinformatics

Mitchell

2014

WIREs Comput Mol Sci

387

343

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Machine learning algorithms are generally developed in computer science or adjacent disciplines and find their way into chemical modeling by a process of diffusion. Though particular machine learning methods are popular in chemoinformatics and quantitative structure–activity relationships (QSAR), many others exist in the technical literature. This discussion is methods-based and focused on some algorithms that chemoinformatics researchers frequently use. It makes no claim to be exhaustive. We concentrate on methods for supervised learning, predicting the unknown property values of a test set of instances, usually molecules, based on the known values for a training set. Particularly relevant approaches include Artificial Neural Networks, Random Forest, Support Vector Machine, k-Nearest Neighbors and naïve Bayes classifiers.

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Section: Machine-learning Methodsmentioning

confidence: 99%

Section: Machine‐learning Methodsmentioning

confidence: 99%

Machine learning methods in chemoinformatics

Mitchell

2014

WIREs Comput Mol Sci

387

343

View full text Add to dashboard Cite

show abstract

“…Nevertheless, several structure‐odor relationships have been successfully established, e.g., for rigid odorants, either to discriminate hedonic qualities or to characterize the odor similarity of odorant mixtures . A crucial point is that the use of sophisticated molecular descriptors is necessary to develop a metric for odorants …”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Tromelin

Chabanet

Audouze

et al. 2017

Flavour & Fragrance J

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The perception of odor is an important component of smell; the first step of odor detection, and the discrimination of structurally diverse odorants depends on their interactions with olfactory receptors (ORs). Indeed, the perception of an odor's quality results from a combinatorial coding, in which the deciphering remains a major challenge.Several studies have successfully established links between odors and odorants by categorizing and classifying data. Hence, the categorization of odors appears to be a promising way to manage odors.In the proposed study, we performed a computational analysis using odor descriptions of the odorants present in Flavor-Base 9th Edition (2013). We converted the Flavor-Base data into a binary matrix (1 when the odor note appears in the odor description, 0 otherwise). We retained 251 odor notes and 3508 odorants, considering only the orthonasal perception. Two categorization methods were performed: agglomerative hierarchical clustering (AHC), and self-organizing map (SOM). AHC was based on a measure of the distance between the elements performed by multidimensional scaling (MDS) for the odorants, and correspondence analysis (CA) for the odor notes.The results demonstrated that the SOM classes appeared to be less dependent on the frequency of the odor notes than those of the AHC clusters. SOMs are especially useful for identifying the associations between less than 4 or 5 odor notes within groups of odorants.The obtained results highlight subsets of odorants sharing similar groups of odor notes, suggesting an interesting and promising way of using computational approaches to help decipher olfactory coding.

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“…Cost-effective evaluation of ½avour is dif¼cult for a varied and large number of fragrance compounds after synthesis. [3][4][5][6][7][8] With the development of the computer-aided molecule design, there is a motivation to study or design the above-mentioned compounds using computer-aided methods. The major advantage of this approach is that correlation can be obtained based on the descriptors derived solely from the molecular structure of the compounds investigated.…”

Section: Introductionmentioning

confidence: 99%

Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

Luan

Liu

Wen

et al. 2008

Flavour & Fragrance J

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Classi¼cation models of the fragrance properties of chemical compounds were performed using linear and non-linear models. The dataset was divided into three classes on the basis of their fragrances: apple, pineapple and rose. The three-class problem was ¼rst explored by a linear classi¼er approach, using linear discriminant analysis (LDA). A more accurate prediction model, the non-linear machine-learning technique, support vector machine (SVM), was subsequently investigated. Descriptors calculated from the molecular structures alone were used to represent the characteristics of compounds. The model containing four descriptors founded by SVM showed better predictive ability than LDA. The accuracy in the prediction for the three datasets was 96.6%, 80.0% and 100% for SVM, respectively. The results indicate that SVM can be used as a powerful modelling tool for QSAR studies and the selected descriptors can represent the fragrances of these chemical compounds.

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Structure–camphor odour relationships using the Generation and Selection of Pertinent Descriptors approach

Cited by 18 publications

References 21 publications

Machine learning methods in chemoinformatics

Machine learning methods in chemoinformatics

Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors

Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

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