Structure-odor Relationships Research Articles

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm.

Determining the structure-odor relationship has always been a very challenging task. The main challenge in investigating the correlation between the molecular structure and its associated odor is the ambiguous and obscure nature of verbally defined odor descriptors, particularly when the odorant molecules are from different sources. With the recent developments in machine learning (ML) technology, ML and data analytic techniques are significantly being used for quantitative structure-activity relationship (QSAR) in the chemistry domain toward knowledge discovery where the traditional Edisonian methods have not been useful. The smell perception of odorant molecules is one of the aforementioned tasks, as olfaction is one of the least understood senses as compared to other senses. In this study, the XGBoost odor prediction model was generated to classify smells of odorant molecules from their SMILES strings. We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. The ML model developed in this study was able to predict all seven basic smells with high precision (>99%) and high sensitivity (>99%) when tested on an independent test dataset. The results of the proposed study were also compared with three recently conducted studies. The results indicate that the XGBoost-PCA model performed better than the other models for predicting common odor descriptors. The methodology and ML model developed in this study may be helpful in understanding the structure-odor relationship.Communicated by Ramaswamy H. Sarma.

A principal odor map unifies diverse tasks in olfactory perception.

Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.

Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics

In this study, using both 2D-QSPR and 3D-QSPR approaches, to understand the structure-odor relationship of 78 1–4-pyrazine odorant molecules and to use this knowledge for the design of new food and flavor products. According to our results, the developed models have good predictability such as the HQSPR/BC model with QLOO2=0.832, R2 = 0.916, CoMSIA/SEH model with Q2 = 0.624, Rcv2 = 0.590, Rncv2 = 0.932, Rbs2 = 0.963, and Topomer CoMFA model withRtraining2 = 0.899, Rtest2 = 0.916. The Monte Carlo method was used in the creation of a Quantitative Structure-Property Relationship (QSPR) model. The molecular structure is represented using optimized Simplified Molecular Input Line Entry System (SMILES) and molecular descriptors. The performance of the model is evaluated using the Correlation Ideality Index (IIC) and the Correlation Contradiction Index (CCI). The best model, designated as TF2, boasts excellent statistical properties with a training R-squared value of 0.957 and a test R-squared value of 0.834. The model was then used to determine promoter activity levels, which formed the basis for the design of 36 new odorant molecules. Molecular docking and pharmacokinetic properties were used to explain the mode of binding between the proposed compounds and the active site of the Porcine Odorant Binding Protein complexed with pyrazine (2-isobutyl-3-methoxypyrazine). Molecular dynamic simulation was used to assess and justify the stability of the ligand in the active site of the receptor. The results of this study provide a basis for the discovery of new compounds with lower olfactory thresholds and diverse pharmacological properties.

Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures.

The mechanisms involved in the homogeneous perception of odorant mixtures remain largely unknown. With the aim of enhancing knowledge about blending and masking mixture perceptions, we focused on structure-odor relationships by combining the classification and pharmacophore approaches. We built a dataset of about 5000 molecules and their related odors and reduced the multidimensional space defined by 1014 fingerprints representing the structures to a tridimensional 3D space using uniform manifold approximation and projection (UMAP). The self-organizing map (SOM) classification was then performed using the 3D coordinates in the UMAP space that defined specific clusters. We explored the allocating in these clusters of the components of two aroma mixtures: a blended mixture (red cordial (RC) mixture, 6 molecules) and a masking binary mixture (isoamyl acetate/whiskey-lactone [IA/WL]). Focusing on clusters containing the components of the mixtures, we looked at the odor notes carried by the molecules belonging to these clusters and also at their structural features by pharmacophore modeling (PHASE). The obtained pharmacophore models suggest that WL and IA could have a common binding site(s) at the peripheral level, but that would be excluded for the components of RC. In vitro experiments will soon be carried out to assess these hypotheses.

OWSum: algorithmic odor prediction and insight into structure-odor relationships

We derived and implemented a linear classification algorithm for the prediction of a molecule’s odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.

Investigation of the structure-odor relationship using a Transformer model

The relationships between molecular structures and their properties are subtle and complex, and the properties of odor are no exception. Molecules with similar structures, such as a molecule and its optical isomer, may have completely different odors, whereas molecules with completely distinct structures may have similar odors. Many works have attempted to explain the molecular structure-odor relationship from chemical and data-driven perspectives. The Transformer model is widely used in natural language processing and computer vision, and the attention mechanism included in the Transformer model can identify relationships between inputs and outputs. In this paper, we describe the construction of a Transformer model for predicting molecular properties and interpreting the prediction results. The SMILES data of 100,000 molecules are collected and used to predict the existence of molecular substructures, and our proposed model achieves an F1 value of 0.98. The attention matrix is visualized to investigate the substructure annotation performance of the attention mechanism, and we find that certain atoms in the target substructures are accurately annotated. Finally, we collect 4462 molecules and their odor descriptors and use the proposed model to infer 98 odor descriptors, obtaining an average F1 value of 0.33. For the 19 odor descriptors that achieved F1 values greater than 0.45, we also attempt to summarize the relationship between the molecular substructures and odor quality through the attention matrix.

Incorporating Machine Learning in Computer-Aided Molecular Design for Fragrance Molecules

The demand for new novel flavour and fragrance (F&F) molecules has boosted the need for a systematic approach to designing fragrance molecules. However, the F&F-related industry still relies heavily on experimental approaches or on existing databases without considering the consequences resulting from changes in concentration, which could omit potential fragrances. Computer-aided molecular design (CAMD) has great potential to identify novel molecular structures to be used as fragrances. Using CAMD for this purpose requires models to predict the olfaction properties of molecules. A rough set-based machine learning (RSML) approach is used to develop an interpretable predictive model for odour characteristics in this work. New rule-based models are generated from RSML based on the dilution and a number of different topological indices which identify the structure-odour relationship of fragrance molecules. The most prominent rules are selected and formulated as constraints in a CAMD optimisation model. The combination of several rules was able to increase the coverage of different classes of molecules. To model the performance indicators that vary over a range of properties, a disjunctive programming model is also incorporated into the CAMD framework. A case study demonstrates the utilisation of this methodology to design fragrance additives in dishwashing liquid. The results illustrate the capability of the novel RSML and CAMD framework to identify potential fragrance molecules that can be used in consumer products.

Decoding Structure–Odor Relationship Based on Hypergraph Neural Network and Deep Attentional Factorization Machine

Understanding the relationship between the chemical structure and physicochemical properties of odor molecules and olfactory perception, i.e., the structure–odor relationship, remains a decades-old, challenging task. However, the differences among the molecular structure graphs of different molecules are subtle and complex, and the molecular feature descriptors are numerous, with complex interactions that cause multiple odor perceptions. In this paper, we propose to decompose the features of the molecular structure graph into feature vectors corresponding to each odor perception descriptor to effectively explore higher-order semantic interactions between odor molecules and odor perception descriptors. We propose an olfactory perception prediction model noted as HGAFMN, which utilizes a hypergraph neural network with the olfactory lateral inhibition-inspired attention mechanism to learn the molecular structure feature from the odor molecular structure graph. Furthermore, existing methods cannot effectively extract interactive features in the large number of molecular feature descriptors, which have complex relations. To solve this problem, we add an attentional factorization mechanism to the deep neural network module and obtain a molecular descriptive feature through the deep feature combination based on the attention mechanism. Our proposed HGAFMN has achieved good results in extensive experiments and will help product design and quality assessment in the food, beverage, and fragrance industries.

Predicting odor from molecular structure: a multi-label classification approach

Decoding the factors behind odor perception has long been a challenge in the field of human neuroscience, olfactory research, perfumery, psychology, biology and chemistry. The new wave of data-driven and machine learning approaches to predicting molecular properties are a growing area of research interest and provide for significant improvement over conventional statistical methods. We look at these approaches in the context of predicting molecular odor, specifically focusing on multi-label classification strategies employed for the same. Namely binary relevance, classifier chains, and random forests adapted to deal with such a task. This challenge, termed quantitative structure–odor relationship, remains an unsolved task in the field of sensory perception in machine learning, and we hope to emulate the results achieved in the field of vision and auditory perception in olfaction over time.

Insight into the Structure-Odor Relationship of Molecules: A Computational Study Based on Deep Learning.

Molecules with pleasant odors, unacceptable odors, and even serious toxicity are closely related to human social life. It is impractical to identify the odors of molecules in large quantities (particularly hazardous odors) using experimental methods. Computer-aided methods have currently attracted increasing attention for the prediction of molecular odors. Here, through models based on multilayer perceptron (MLP) and physicochemical descriptors (MLP-Des), MLP and molecular fingerprint, and convolutional neural network (CNN), we conduct the two-class prediction of odor/no odor, fruity/no odor, floral/no odor, and woody/no odor, and the multi-class prediction of fruity/flowery/woody/no odor on our newly refined molecular odor datasets. We show that three kinds of predictors can robustly predict molecular odors. The MLP-Des model not only exhibits the best prediction results (the AUC values are 0.99 and 0.86 for the two- and multi-classification models, respectively) but can also well reflect the characteristics of the structure–odor relationship of molecules. The CNN model takes 2D molecular images as input and can automatically extract the structural features related to molecular odors. The proposed models are of great help for the prediction of molecular odorants, understanding the underlying relationship between chemical structure and odor perception, and the discovery of new odorous and/or hazardous molecules.

Odor prediction and aroma mixture design using machine learning model and molecular surface charge density profiles

Structure-Odor Relationship (SOR) is the key to the understanding of the mechanism of odors, which further facilitates the odor prediction and aroma design. However, the modeling of SOR is still a challenge since the understanding of odor mechanism is unresolved. In this paper, an Artificial Neural Network (ANN) model is developed to generate the SOR model for aroma mixtures, in which the molecular surface charge density profiles (σ-profiles) are used as the descriptors. In this ANN model, the aroma mixture properties are the input, and the σ-profiles of the mixtures are the output, which can directly predict the σ-profiles of the aroma mixtures under certain odor requirements. Then, Euclidean distance-based ingredient screening method is applied to search the aroma mixture that fits the predicted σ-profiles from the ANN model. Finally, a Computer-Aided Aroma Design (CAAD) framework is formulated for the design of aroma mixtures. Two case studies of aroma mixture design are presented. The results are verified by the electronic nose, which highlight the effect of the developed SOR model as well as the CAAD framework.

Smell compounds classification using UMAP to increase knowledge of odors and molecular structures linkages.

This study aims to highlight the relationships between the structure of smell compounds and their odors. For this purpose, heterogeneous data sources were screened, and 6038 odorant compounds and their known associated odors (162 odor notes) were compiled, each individual molecule being represented with a set of 1024 structural fingerprint. Several dimensional reduction techniques (PCA, MDS, t-SNE and UMAP) with two clustering methods (k-means and agglomerative hierarchical clustering AHC) were assessed based on the calculated fingerprints. The combination of UMAP with k-means and AHC methods allowed to obtain a good representativeness of odors by clusters, as well as the best visualization of the proximity of odorants on the basis of their molecular structures. The presence or absence of molecular substructures has been calculated on odorant in order to link chemical groups to odors. The results of this analysis bring out some associations for both the odor notes and the chemical structures of the molecules such as “woody” and “spicy” notes with allylic and bicyclic structures, “balsamic” notes with unsaturated rings, both “sulfurous” and “citrus” with aldehydes, alcohols, carboxylic acids, amines and sulfur compounds, and “oily”, “fatty” and “fruity” characterized by esters and with long carbon chains. Overall, the use of UMAP associated to clustering is a promising method to suggest hypotheses on the odorant structure-odor relationships.

Studies on analogs of DAMASCENOLIDETM: Part 4. Synthesis and odor evaluation of sulfur-containing analogs of DAMASCENOLIDETM

DAMASCENOLIDETM [1, 4-(4-methylpent-3-en-1-yl)furan-2(5H)-one], which is isolated from damask rose, is a useful aroma compound with a citrus-like odor. We have previously reported on the synthesis and odor properties of 34 analogs of 1 as part of our new aroma compound development project. In order to develop better aroma compounds and to gather more information on structure-odor relationships, 6 novel sulfur-containing analogs of 1 were synthesized. Odor evaluation revealed that their odors differed significantly from those of the corresponding sulfur-free compounds. The introduction of a sulfur atom does not necessarily result in a sulfur-like odor. In particular, the 2(5H)-thiophenone analogs gave waxy, oily, and lactone-like odors that are uncharacteristic of sulfur-containing compounds. In many synthesized analogs, the introduction of a sulfur atom led to an increase in odor intensity, as expected.

Studies on analogs of DAMASCENOLIDETM: Part 3. Synthesis and odor evaluation of dimethylated, cyclopropanated, and other analogs of DAMASCENOLIDETM.

DAMASCENOLIDETM [1, 4-(4-methylpent-3-en-1-yl)furan-2(5H)-one], which has a citrus-like odor, is an important aroma component of roses. We have previously reported on the synthesis and odor evaluation of 24 analogs of 1 as part of our new aroma compound developing project. To accumulate more information on structure-odor relationships, 10 more promising analogs such as dimethylated and cyclopropanated analogs were synthesized and subjected to odor evaluation. As a result, it was found that dimethylation of the furanone ring affected the odor. It was also found that cyclopropanation of 1 affected the odor, whereas cyclopropanation of the double bond isomer of 1 did not significantly affect the odor. The effects on the odor caused by ring size expansion and replacement of the side chain were also investigated.

Synthesis and Olfactory Characteristics of Hydroxyether Derivatives of Methyl Eugenol

Methyl eugenol is a naturally occurring substance found in the essential oils of several plant species. It is a cheap and abundant material with a rather limited interest as perfuming ingredient. This research investigated the structure-odour relationship of methyl eugenol and its derivatives, focusing on the chemical transformation of the double bond to hydroxyether derivatives in a controlled manner. Two derivatives 2-(4-ethyl- 3-methoxybenzyl)oxirane (68.8% yield) and 1-(3,4-dimethoxyphenyl)-3-ethoxypropan-2-ol (63.58% yield), were synthesized from methyl eugenol. This research found that the odour characteristic of 2-(4-ethyl-3-methoxybenzyl)oxirane[floral, fresh, slight lime and sweet]compounds, is clearly different from the odours of the 1-(3,4-dimethoxyphenyl)-3-ethoxypropan-2-ol [Glue-like, pungent, and Caramel-like]. It is evident from our observations that double bond substitution and functional alteration of the methyl eugenol modifies the perceived odour of methyl eugenol derivative. Thus, structural modification overall led to an increase in odour potency. 
 Keywords: Fragrance, Methly-Eugenol, Ether, Olfactory characteristic, Epoxidation.

Analog synthesis of DAMASCENOLIDETM, an important aroma component of roses, and their odor properties.

DAMASCENOLIDETM [1, 4-(4-methylpent-3-en-1-yl)furan-2(5H)-one], which has a citrus-like odor, is an important aroma component of roses. We have previously reported on the synthesis and odor evaluation of double-bond isomers of 1 and concluded that the position and the geometric isomerism of the double-bond had a significant effect on the odor. For the purpose of deepening knowledge about structure-odor relationships, we synthesized 13 analogs of compound 1 and evaluated their odors. As a result, it was found that the presence of two double-bonds and branched methyl group at the terminal position in the side chain was essential in order to have a citrus-like odor. Substitution of the side chain with appropriate length at the appropriate 4-position of the 2(5H)-furanone ring was also an important factor in determining the quality of the odor.

Structure-Odor Relationships of Vanillin Derivatives

Structure-Odor Relationships of Vanillin Derivatives

Structure-Odor Relationships of the Main Vetiver Component Khusimol and its Derivatives

Structure-Odor Relationships of the Main Vetiver Component Khusimol and its Derivatives

Chirality: An Important Phenomenon Regarding Biosynthesis, Perception, and Authenticity of Flavor Compounds.

Chirality plays an important role in flavor research. This will be outlined using selected examples from the key areas analysis, authenticity assessment, biogenesis, and odor perception. Developments of analytical techniques, in particular the use of chiral stationary phases in capillary gas chromatography, enabled the determination of naturally occurring configurations of chiral volatiles at trace levels. Thus, knowledge of pathways and enzymes involved in the biogenesis of chiral substances was acquired, and enantioselective analysis has become a prominent tool in the authenticity assessment of flavorings. Increasing information is also available on structure-odor relationships of chiral flavor compounds and the influence of their configurations on odor thresholds and odor properties. A substantial extension of these data sets and a further understanding of the role of chirality in the perception of aroma compounds is expected from studies on the enantioselectivity of odorant receptor systems. Developments in these areas will be put into a historical perspective, recent progress will be emphasized, and data gaps will be described.

Structure-Odor Activity Studies on Derivatives of Aromatic and Oxygenated Monoterpenoids Synthesized by Modifying p-Cymene.

Thymoquinone was recently reported as having a unique pencil-like odor and being the impact compound for the cedar-like and cedar wood-based product smell such as pencils. The compounds thymol and carvacrol are structurally related odorants commonly found in plants and foods such as thyme and oregano, also having a significant contribution to their overall aroma. However, a systematic elucidation of the sensory properties in this class of oxygenated, aromatic monoterpenoids has not been carried out. To close this gap and gain new insights into structure-odor relationships leading to pencil-like and woody odors, 19 structurally related derivatives of p-cymene starting from thymol and carvacrol were synthesized and characterized. The compounds had odor thresholds ranging from 2.0 ng/L air to 388.8 ng/L air, being lowest for thymol and carvacrol and highest for thymohydroquinone. The compounds smelled mostly thyme-like, oregano-like, and pencil-like with phenolic, earthy, and medicinal variations in their odor character, which could be successfully linked to structural motifs.