Perfumery Radar 2.0: A Step toward Fragrance Design and Classification

Influence of Multiple Binding Sites on the Supramolecular Assembly of N-[(3-pyridinylamino) Thioxomethyl] Carbamates

In this study, we investigated how the presence of multiple intermolecular interaction sites influences the heteromeric supramolecular assembly of N-[(3-pyridinylamino) thioxomethyl] carbamates with fluoroiodobenzenes. Three targets—R-N-[(3-pyridinylamino) thioxomethyl] carbamate (R = methyl, ethyl, and isobutyl)—were selected and crystallized, resulting in three parent structures, five co-crystals, and one co-crystal solvate. Three hydrogen-bonded parent crystal structures were stabilized by N-H···N hydrogen bonding and assembled into layers that stacked on top of one another. Molecular electrostatic potential surfaces were employed to rank binding sites (Npyr > C=S > C=O) in order to predict the dominant interactions. The N-H⋯H hydrogen bond was replaced by I⋯Npyr in 3/6 cases, I⋯C=S in 4/6 cases, and I⋯O=C in 1 case. Interestingly, the I⋯C=S halogen bond coexisted twice with I⋯Npyr and I⋯O=C. Overall, the MEPs were fairly reliable for predicting co-crystallization outcomes; however, it is crucial to also consider factors such as molecular flexibility. Finally, halogen-bond donors are capable of competing for acceptor sites, even in the presence of strong hydrogen-bond donors.

Synthesis of Cyclic Fragrances via Transformations of Alkenes, Alkynes and Enynes: Strategies and Recent Progress

With increasing demand for customized commodities and the greater insight and understanding of olfaction, the synthesis of fragrances with diverse structures and odor characters has become a core task. Recent progress in organic synthesis and catalysis enables the rapid construction of carbocycles and heterocycles from readily available unsaturated molecular building blocks, with increased selectivity, atom economy, sustainability and product diversity. In this review, synthetic methods for creating cyclic fragrances, including both natural and synthetic ones, will be discussed, with a focus on the key transformations of alkenes, alkynes, dienes and enynes. Several strategies will be discussed, including cycloaddition, catalytic cyclization, ring-closing metathesis, intramolecular addition, and rearrangement reactions. Representative examples and the featured olfactory investigations will be highlighted, along with some perspectives on future developments in this area.

Imputation of sensory properties using deep learning

Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network. The imputation model achieved a substantially higher accuracy of prediction, with improvements in R² between 0.26 and 0.45 over the next best method for each sensory property. We also demonstrate that robust uncertainty estimates generated by the imputation model enable the most accurate predictions to be identified and that imputation also more accurately predicts activity cliffs, where small changes in compound structure result in large changes in sensory properties. In combination, these results demonstrate that the use of imputation, based on data from less expensive, early experiments, enables better selection of compounds for more costly studies, saving experimental time and resources.

Multivariate Analysis of Olfactory Profiles for 140 Perfumes as a Basis to Derive a Sensory Wheel for the Classification of Feminine Fragrances

In order to guide consumers in their purchase of a new fragrance, one approach is to visualize the spectrum of men’s or women’s fragrances on a two-dimensional plot. One of such sensory maps available is the Hexagon of Fragrance Families. It displays 91 women’s perfumes inside a polygon, so that each side accounts for a different olfactory class. In order to discuss this chart, odor profiles were obtained for these fragrances and additional feminine ones (140 in total, launched from 1912 to 1990). An olfactory dataset was arranged by coding numerically the descriptions obtained from Fragrantica and Osmoz websites, as well as from a perfume guide. By applying principal component analysis, a sensory map was obtained that properly reflected the similarities between odor descriptors. Such representation was equivalent to the map of feminine fragrances called Givaudan Analogies, comprised of five major categories. Based on the results, a modified version of the Hexagon based on 14 categories was proposed. The first principal component explained preference for daytime versus nighttime wear, and regression models were fitted in order to estimate such preferences according to the odor profiles. The second component basically discriminated floral versus chypre (mossy–woody) fragrances. Results provide a fundamental basis to develop standard sensory maps of women’s fragrances.

Use of the normalized hydrophilic-lipophilic-deviation (HLDN) equation for determining the equivalent alkane carbon number (EACN) of oils and the preferred alkane carbon number (PACN) of nonionic surfactants by the fish-tail method (FTM)

The standard HLD (Hydrophilic-Lipophilic-Deviation) equation expressing quantitatively the deviation from the "optimum formulation" of Surfactant/Oil/Water systems is normalized and simplified into a relation including only the three more meaningful formulation variables, namely (i) the "Preferred Alkane Carbon Number" PACN which expresses the amphiphilicity of the surfactant, (ii) the "Equivalent Alkane Carbon Number" EACN which accurately reflects the hydrophobicity of the oil and (iii) the temperature which has a strong influence on ethoxylated surfactants and is thus selected as an effective, continuous and reversible scanning variable. The PACN and EACN values, as well as the "temperature-sensitivity-coefficient"τ of surfactants are determined by reviewing available data in the literature for 17 nonionic n-alkyl polyglycol ether (C_iE_j) surfactants and 125 well-defined oils. The key information used is the so-called "fish-tail-temperature" T* which is a unique data point in true ternary C_iE_j/Oil/Water fish diagrams. The PACNs of C_iE_j surfactants are compared with other descriptors of their amphiphilicity, namely, the cloud point, the HLB number and the PIT-slope value. The EACNs of oils are rationalized by the Effective-Packing-Parameter concept and modelled thanks to the COSMO-RS theory.

Understanding the Perceptual Spectrum of Commercial Perfumes as a Basis for a Standard Sensory Wheel of Fragrances

Given the enormous number of perfumes available on the market, it is of interest to guide consumers in their purchase of a new fragrance. One approach is to project the multidimensional perceptual space of scents on a two-dimensional sensory map based on meaningful dimensions. One of the pioneering studies on this issue mapped 94 commercial perfumes according to two axes. Such an odor map is discussed here in detail by applying Principal Component Analysis to the numeric odor description of 176 fragrances. Quantitative odor profiles were obtained from Fragrantica’s website and three fragrances guides published by Haarmann & Reimer, Michael Edwards, and the French Society of Perfumers. A sensory map was obtained that reflected the similarities and dissimilarities between those odor descriptors most commonly used in perfumery. This representation was consistent with other related plots that have been previously reported. One dimension discriminated between fragrances targeted at men versus women. An orthogonal factor distinguished perfumes for daytime versus nighttime wear. These ratings, as well as seasonal preferences, could be estimated based on the main odor character attributes applied to describe the scent. The results provide a scientific basis for the comprehensive classification of commercial perfumes compiled by Edwards according to his famous “Fragrance Wheel”.

Machine-Learning-Based Olfactometer: Prediction of Odor Perception from Physicochemical Features of Odorant Molecules

Gas chromatography/olfactometry (GC/O) has been used in various fields as a valuable method to identify odor-active components from a complex mixture. Since human assessors are employed as detectors to obtain the olfactory perception of separated odorants, the GC/O technique is limited by its subjectivity, variability, and high cost of the trained panelists. Here, we present a proof-of-concept model by which odor information can be obtained by machine-learning-based prediction from molecular parameters (MPs) of odorant molecules. The odor prediction models were established using a database of flavors and fragrances including 1026 odorants and corresponding verbal odor descriptors (ODs). Physicochemical parameters of the odorant molecules were acquired by use of molecular calculation software (DRAGON). Ten representative ODs were selected to build the prediction models based on their high frequency of occurrence in the database. The features of the MPs were extracted via either unsupervised (principal component analysis) or supervised (Boruta, BR) approaches and then used as input to calibrate machine-learning models. Predictions were performed by various machine-learning approaches such as support vector machine (SVM), random forest, and extreme learning machine. All models were optimized via parameter tuning and their prediction accuracies were compared. A SVM model combined with feature extraction by BR-C (confirmed only) was found to afford the best results with an accuracy of 97.08%. Validation of the models was verified by using the GC/O data of an apple sample for comparison between the predicted and measured results. The prediction models can be used as an auxiliary tool in the existing GC/O by suggesting possible OD candidates to the panelists and thus helping to give more objective and correct judgment. In addition, a machine-based GC/O in which the panelist is no longer needed might be expected after further development of the proposed odor prediction technique.

Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.