Abstract
There has been recent progress in predicting whether common verbal descriptors such as “fishy”, “floral” or “fruity” apply to the smell of odorous molecules. However, accurate predictions have been achieved only for a small number of descriptors. Here, we show that applying naturallanguage semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for monomolecular odorants over a large and potentially arbitrary set of descriptors. This is noteworthy given that the prevailing view is that humans’ capacity to identify or characterize odors by name is poor. We successfully apply our semanticsbased approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descriptors, a tenfold increase in the number of descriptors from previous attempts. These results imply that the semantic distance between descriptors defines the equivalent of an odorwheel.
Similar content being viewed by others
Introduction
Humans are unique in their capacity to express sensory perceptual experiences using the powerful machinery of language—e.g. “The Requiem is Mozart’s most mournful work”, “I feel a stabbing more than a burning pain”, which prompts the question: to what extent can language adequately convey perception? This question is particularly contentious in the realm of olfaction research, where quantifying odor percepts through semantic attributes is a central endeavor^{1,2}. Indeed ratings of a molecule along a comprehensive set of descriptors such as “putrid”, “floral”, and “apple” could uniquely characterize the molecule’s odor^{1}, and experts spend considerable time and effort handcrafting domainspecific sets of odor descriptors or collecting ratings for large numbers of descriptors for each molecule of interest^{3,4}. A standard, generally applicable set of “primary” odor descriptors would be more amenable^{5} but despite decades of research this effort has been in vain^{2,6}. The prevailing view is that there is a significant disconnect between humans’ strong capacity for odor discrimination^{7} and their inability to identify or characterize odors by name^{1,8,9,10,11,12}. This would seem to suggest that semantic descriptors cannot be reliable. Yet semantically generated multidimensional descriptors have been proven to be stable^{13} and there is substantial evidence of interactions between language and various perceptual modalities including olfaction^{14,15,16,17,18,19}. Recent work even suggests that olfactory knowledge can improve the performance of linguistic representations in predicting human similarity judgments^{20}, while linguistic representations can be applied to quantify the olfactory specificity and familiarity of words^{6}. Most recent work uses the linguistic approach to predict a reduced representation, via clustering, of the odor of a molecule^{21}; however, the predictive efficacy of this model falls abruptly when more than 5 clusters of descriptors are considered.
We here show that applying naturallanguage semantic representations on a small set of general olfactoryperceptual descriptors can allow for the accurate inference of perceptual ratings for monomolecular odorants over a large and potentially arbitrary set of descriptors. Furthermore, combining such semanticbased perceptual ratings predictions with a moleculetoratings model that relies on chemoinformatic features, we perform zeroshot learning inference^{20,22} of perceptual ratings for arbitrary molecules.
Results
Correspondence between semantic space and olfactory ratings space
To investigate whether semantic representations derived from language use could be applied to reliably predict how molecules are rated along a large set of detailed olfactoryperceptual descriptors, we chose to predict the ratings of 146 finegrained odor descriptors of the well known Dravnieks dataset (Fig. 1)^{23}. The ratings are obtained by asking human raters to assign values, on a fixed scale, of how close their perceptual experience of smelling an odorant is to each one of the descriptors (see Methods). As a starting point to learn a generalizable semanticperceptual model, we used the ratings from the 19 general descriptors of a different study, the DREAM dataset^{24} as it has 58 molecules in common, from 128 in total, and shares 10 descriptors with the Dravnieks dataset (Fig. 1 and Supplementary Table 1). To quantify the semantic relationship between the DREAM and Dravnieks descriptors, we used a representation of linguistic data known as distributional semantic models. These models are quantitative, datadriven, vectorial representations of word meaning motivated by the distributional hypothesis, which asserts that the meaning of a word can be inferred as a function of the linguistic contexts in which it occurs^{25}. A distributional semantic model assigns a vector to each word in a lexicon, based on the word’s use in language; words that are used in similar contexts, thus assumed to be more semantically similar, have vectors that are closer together in the distributional semantic space of the model (Fig. 1b). In particular, we utilized publicly available 300dimensional semantic vectors produced using the fastText skipgram algorithm that were trained on a corpus of over 16 billion words^{26}. The fastText model contained vectors corresponding to the 19 DREAM descriptors which we refer to as the DREAM semantic vectors, and to 131 of the 146 Dravnieks descriptors which we refer to as the Dravnieks semantic vectors. Note that the training corpus was not biased in any way to include more or less olfaction or perceptionrelated material, i.e., it was intended to represent the general structure of semantic knowledge.
Given that the DREAM and Dravnieks studies presented different sets of descriptors to the subjects, we expect that the perceptual ratings for the molecules in common will be reanchored according to the available descriptors, and consequently that the descriptor ratings for the two datasets will differ even on shared descriptors^{27}. Indeed we find that, although the correlations across the 58 shared molecules are high for the 19 corresponding descriptors (Fig. 2a), the highest correlation is not always between the matching descriptors: e.g., although “sweet” in DREAM is most highly correlated to “sweet” in Dravnieks, “fruit” has a higher correlation to “peach” than to “fruity” (Supplementary Table 1). Nonetheless, the clusters of highly correlated descriptors defined by the dendrogram follow the close semantic relationship between the descriptors—e.g., “flower” from DREAM correlates highly with the coclustered “rose”, “violets”, “incense”, “perfumery”, “cologne”, “floral”, and “lavender” from Dravnieks.
We compared the correlation matrix based on the descriptors’ perceptual ratings (Fig. 2a) to a correlation matrix between the DREAM and Dravnieks semantic vectors (Fig. 2b). We observe that the two correlation matrices are similarly structured (Procrustes dissimilarity p < 0.05 tested against randomized surrogates, correlation between maxima across the DREAM descriptors is r = 0.74, p < 10^{−4} and r = 0.5, p < 10^{−9} across Dravnieks descriptors). This is also reflected in the semantic vector correlation matrix where “sweet” is similarly maximally correlated with “sweet” in Dravnieks and “fruit” correlation is with “peach” and “citrus” than with “fruity”. Finally although “flower” shares a large weight with “floral”, it has similar correlation with “strawberry”, “fragrant”, and “lavender” (Fig. 2b, top). Further insight is gained from looking at arrangement changes of twodimensional projections of the DREAM descriptors based on their ratings distance (Fig. 2c) and their semantic distance (Fig. 2d; also see Methods). Notably, we observe only small local distortions of group mappings, e.g., “grass”, “flower” and “fruit” contiguous in both spaces (pink). However, there is also a global distortion as “sweet” is arranged in the semantic space near its antonym “sour,” and in the ratings space “sweet” is arranged closer to the perceptually similar term “bakery,” and “sour” is arranged closer to the perceptually similar term “decayed”.
Extending predictions to arbitrary descriptors
The similarities in how the descriptors are arranged in the olfactoryperceptual space and in the semantic space favor the hypothesis of a tight perceptuallinguistic bond between the descriptors ratings and their linguistic meanings. Consequently we developed a model that learns a transformation S from the 19 DREAM semantic vectors to the 131 Dravnieks semantic vectors (Fig. 3a, top left) and refer to this model as the direct semantic model. We hypothesized that, given the correspondences between the perceptual and semantic spaces, we could use this same matrix S to predict the ratings of the 131 Dravnieks descriptors based solely on the ratings of the 19 DREAM descriptors and the semantic relation between the DREAM and Dravnieks descriptors. We compared the results of the semantic model to a direct ratings model that uses a training set of molecules for which both DREAM and Dravnieks ratings are available to learn a transformation R that can predict a new molecule’s ratings on the Dravnieks descriptors, given its ratings on the DREAM descriptors (Fig. 3a, top right). To further investigate the complementarity of the information provided by the semantic vectors and ratings data, we also looked at the performance of a mixed model that averaged the predictions of the two models.
To avoid overfitting, we used a crossvalidation procedure where the 58 shared molecules are repeatedly divided at random into test sets and training sets and results averaged over repetitions. The performance of all three models was evaluated as the number of training molecules is varied. We compared each model’s performance by computing the median of the correlation between the predicted ratings and the actual ratings for a test set of molecules, across the Dravnieks descriptors. As ratings of molecules across descriptors are significantly correlated, we defined as an appropriate baseline prediction the mean rating for each descriptor across all molecules used for training the model and found that this baseline correlation is around 0.6. We then calculate a Zscore that compares the difference between the baseline correlation and the correlations produced by the models, taking into account their dependence. We report the median Zscore across molecules and across repetitions of crossvalidation.
Remarkably, without making use of any of the ratings from the target set, i.e., an instance of zeroshot learning^{22}, the semantic model is able to predict the ratings in the target set reasonably well (Fig. 3a bottom and 3b inset) with a median Z = 3.7, r = 0.47, p < 10^{−4} (onesided ttest, see Supplementary Fig. 1 for correlations plot) and better than the ratings model when trained on fewer than 6 overlapping molecules (Fig. 3a bottom, blue and gold lines). Furthermore, the mixed model showed excellent performance with a Zscore of up to 5 and was never outperformed by the ratings model, underscoring the importance of the contribution from the semantic model and suggesting complementarity between information available in the ratings and the semantic model (Fig. 3a bottom, green and gold lines).
Extending predictions to arbitrary molecules
We extended this approach using a chemoinformaticstoperception model that allows the prediction of ratings along the 19 DREAM descriptors for any molecule using its molecular features^{24}. We used an imputation model C, pretrained with the DREAM dataset, to predict the 70 Dravnieks molecules that are not part of the DREAM dataset (Fig. 3b, top row; see also Methods). C is then combined with either the semantic transformation S to yield the imputed semantics model or used to train R yielding an imputed ratings model, both inferring Dravnieks ratings (Fig. 3b, middle row). These models were also averaged to produce a mixed model and scored on Dravnieks ratings (Fig. 3b, bottom row).
Once again, predictions of descriptor ratings based on the semantic vectors alone with no molecular training data, are significantly better than chance when no training molecules are available (Fig. 3b, bottom, median Z = 3.4, r = 0.40, p < 0.001, see methods—see plot inset and Supplementary Fig. 2 for correlations plot) and outperform the imputed ratings model when less than 10 molecules are available for training (Fig. 3b, bottom, blue and gold lines). We also again observe that a mixed model dominates the ratings model, showcasing the utility of semantic vectors even when ratings for a training set of molecules are available (Fig. 3b, bottom, gold and green lines). This advantage persists even as the number of molecules for which the source ratings available grows larger.
Analysis of predictive performance
To understand the performance of the semanticsbased models, we varied the number of source DREAM descriptors whose semantic vectors are available for training the direct and imputed semantic models while using leaveoneout crossvalidation on their respective training/test molecule sets. The method we used for prioritizing the 19 perceptual descriptors is a stateoftheart prototype selection algorithm based on a nonnegative constrained reconstruction of the original data (see Methods and ref.^{28}). We chose this approach as it selects recursively the best individual descriptor, i.e. the descriptor that best explains the entire perceptual ratings data, as opposed to commonlyused dimensionalityreduction factorization algorithms. We observe that for both models, as the number of source descriptors increases, prediction performance generally increases, though the performance improvements plateau twice at four source descriptors, notably “sour”, “urinous”, “burnt” and “sweet”, and then around ten source descriptors (Fig. 4a). The direct semantic model uses real DREAM ratings for making its predictions and so its correlation across descriptors is overall higher and the difference grows at the second plateau (Fig. 4a, squares and circles). This also suggests that it is possible to achieve good prediction performance on the target descriptors’ predictions by collecting only a small number of ratings from a smaller number of source descriptors.
We analyzed the quality of model predictions for each of the 58 overlapping molecules of this leaveoneout model (last green dot in Fig. 3a and see Supplementary Data 1 for all the predictions) and find that the mixed model is more stable across molecules than the semantic model and as expected its correlations are also higher, around 0.8 on average (Fig. 4b).
Notably the semantic model predicted the perceptual ratings profile for 57 of the 58 shared molecules with significantly abovechance correlations. The bestpredicted molecule pyridine, with a fishlike smell, had a correlation around 0.6 while the other top five predicted molecules had herbal and fruitlike smells (Fig. 4b). We also analyzed the quality of the semantic model predictions for each of the 131 Dravnieks descriptors by displaying the median correlation across molecules for each descriptor in a histogram (Fig. 4c, left). Notably about 30 percent of descriptors were predicted with a correlation higher than 0.5 for the semantic model, a value that increased to 50 percent of the descriptors for the mixed model (Fig. 4c, right).
Organization of descriptors in semantic and olfactory ratings spaces
A closer look at the nature of the semantic and perceptual ratings spaces yields a deeper intuition about why and how our method works. Figure 4d shows a dendrogram where Dravnieks descriptors are arranged according to semantic distance, and colorcoded by prediction performance of the DirSem model. The prediction’s smoothness reveals an odorwheellike organization whose backbone is the semantic content of the descriptors: the semantic model for a given descriptor is significantly correlated with the prediction performance of the nearest neighboring descriptor in semantic space (r = 0.4170, permutation test p < 0.001) and conversely a descriptor’s location in semantic space well predicts the prediction performance of the semantic model for that descriptor (p < 0.001 measured using 1 and 2nearestneighbors permutation tests). On the other hand, the incomplete correspondence between the semantic and olfactory spaces (Fig. 2c, d) is reflected in the failure to incorporate higherorder semantic concepts such as synonymy/antonymy, meronymy/hypernymy, which could be leveraged to improve our model^{29}.
Universality and flexibility of the model: Prediction of homologous series
To demonstrate the universality and flexibility of our zeroshot learning inference, we applied it to odor molecules that have been extensively studied by fragrance chemists and whose structureodor relationship heuristics are well known. For this, we compiled notes on the smells of 35 molecules containing between two and ten carbon atoms in the homologous series of alkyl aldehydes, primary alcohols, 2ketones, and carboxylic acids (Methods)^{30}. For each molecule, using the chemoinformatic and then semantic model method described above, we computed a prediction of the ratings for each of the 80 unique descriptors extracted from the smell notes (Supplementary Data 2). We then ordered for each molecule the descriptors according to their ratings and computed the areaunderthecurve of the receiveroperatingcharacteristic curve (AUC) on the binary classification task of predicting whether the paradigm odors for each molecule contains the ordered descriptors (Fig. 5). Notably, the family of acids were the bestpredicted family with a median AUC across molecules of 0.75 (p < 0.02 onesided ttest), ketones had an AUC of 0.67 (p < 0.05 onesided ttest), alcohols had an AUC of 0.63 (p < 0.07 onesided ttest) and aldehydes were the worst predicted with an AUC of 0.61 (p < 0.09 onesided ttest). The overall median AUC across families of molecules was 0.66 (p < 0.05 onesided ttest). Acids were overall predicted as sour but as the number of carbons increased the secondranked descriptor changed from pungent to sweaty, musty and then back to pungent. Alcohols had overall an herbal smell and changed from sour to sweet (Supplementary Data 2), aldehydes changed from pungent to sweet and fruity, finally 2ketones changed from sour, acidic to sweet and grape. Here again we encounter the limitations of our current semantic model, reflected in the fact that synonymous odor descriptors have systematically different ranks. For example, each of the 35 molecules is predicted to be more “pungent” than “penetrating” with the median rank of “pungent” being 4 and the median rank of “penetrating” being 54 out of 80.
Discussion
There is a substantial body of evidence suggesting that the representations of words in semantic vector spaces obtained from cooccurrence statistics can be used to model different aspects of human behavior^{31,32,33,34,35,36}. Distributional semantic models not only provide a good prediction of human word similarity judgments^{31,32}, but also other psycholinguistic phenomena such as word acquisition in children^{33}, reaction times in tasks to decide whether a string of letters is a real word or not^{37}, brain activity as measured via fMRI^{34}, reading comprehension^{35,36}, test scores on freeform essays^{33}, and the presence of cognitive impairments associated with prose recall deficiencies, among others^{38}.
The present work demonstrates that the general structure of semantic knowledge, as manifested in the unbiased distribution of words in written language, can in fact be mapped onto the olfactory domain, creating a natural classification of olfactory descriptors, an odorwheel, that speaks to the depth of the connection between language and perception^{14,15,16,17,18,19}.
This connection can be harnessed to effectively transform ratings from a small set of general descriptors to a larger more specific one. In combination with a chemoinformaticstoperception model, our work enables endtoend prediction of perceptual ratings for chemicals for which no ratings data are available at all, that is, a universal predictive map of olfactory perception. Given that specialists including tea and wine tasters, beer brewers, cuisine critics and perfumers expend considerable labor to set up lexicons that are concise and hierarchical, and which cover the relevant odor perception space, a general solution for predicting smell perceptual descriptors, independently of the lexicon used, would be extremely useful across a wide range of industries. Moreover, our findings are also clinically relevant, given that changes in olfactory perception are one of the first signatures of Alzheimer’s Disease^{39} and associated with a range of other mental disorders^{40}. Our approach provides a means to assess directly how these perceptual disturbances are associated with cognitive and emotional states.
Several limitations of the current approach need to be mentioned, along with possible ways to overcome them. In the first place, the model needs to be extended to mixtures of molecules; a naive linear superposition may suffice, but there is strong evidence that mixtures are particularly susceptible to nonlinear interactions^{41}. Secondly, as already mentioned, it is possible to enlarge the basic distributional semantic model with additional lexical structure not easily captured by contextassemantics hypothesis, such as synonyms/antonyms, partofspeech markers such as verbs and nouns, so as to minimize the distortions we observed in the semantictoperception mapping. Related to this last issue, it remains to be seen how the wordbased approach presented here will be extended to unconstrained discourse, in particular as it pertains to the expected difference between open narratives of the olfactoryperceptual experience by smell experts and untrained raters^{42}. We hope that, for all these extensions, our work will provide a foundation to build upon.
Methods
Perceptual data
In all of our experiments, we predict the average perceptual ratings given to molecules in the Dravnieks human olfaction dataset^{23}. This dataset consists of the average ratings of 128 pure molecules by a total of 507 olfaction experts using 146 verbal descriptors. Each molecule was rated only by a subset of 100–150 of the experts. The ratings are on a scale from 0 to 5, where 5 signifies the best match of a descriptor for a given stimulus. Of these 146 descriptors, 15 were discarded because there was no corresponding word vector in our distributional semantic model (e.g., “burnt rubber”), leaving us with 131 descriptors.
Several of our models make use of the data collected by Keller and Vosshall^{43} as presented in Keller et al.^{24} Data from 49 individuals were used, all of the work reported focuses on predicting the ratings averaged across subjects. Individuals were asked to rate each stimulus using 21 perceptual descriptors (intensity, pleasantness, and 19 descriptors), by moving an unlabeled slider. The default location of the slider was 0. The stimuli were 476 pure molecules. For each task, the final position of the slider was translated into a scale from 0 to 100, where 100 signifies the best match of a descriptor for a given stimulus. Further details on the psychophysical procedures and all raw data are available in the Keller and Vosshall article^{43}.
Distributional semantic model
To assess accurately the semantic similarity between the DREAM and Dravnieks descriptors, we took advantage of a distributional semantic model trained using the fastText skipgram algorithm, a neural networkbased model that predicts word occurrence based on context^{44}. These 300dimensional vectors were trained on a corpus of 16 billion words, and are publicly available (https://fasttext.cc/docs/en/englishvectors.html). See Bojanowski et al. for additional details on training and the specifics of the model. We originally used Word2Vec vectors^{26}, and though we saw improved performance with fastText, the difference was quite small.
The semantic vectors of a distributional semantic model are vectorial representations of word meaning motivated by the distributional hypothesis stating that the meaning of a word can be inferred as a function of the linguistic contexts in which it occurs^{25}.
Distributional semantic models rest on the assumption that, to quote Wittgenstein, that ‘the meaning of a word is its use in the language’^{45}. For example, the distributional hypothesis would predict that kitten and cat have similar meanings, given that they are both used in contexts such as the ____ purred softly and the ____ licked its paws; meanwhile the meaning of rock would be less similar to kitten, because it is rarely if ever used in similar contexts. The distributional hypothesis has inspired the field of distributional semantics, which aims to quantify the meanings of words based on cooccurrence statistics of the words in large samples of written or spoken language. These cooccurrence statistics can be summarized and embedded in a lowdimensional vector space, known as a semantic vector space, using dimensionalityreduction techniques such as principal components analysis^{46} or neural networks^{26}. The semantic vector space is constructed in such a way that words that occur in similar contexts and are therefore presumably semantically similar are represented by vectors that are geometrically close as measured for example by cosine distance or Euclidean distance.
Chemoinformatic features
We used version six of the Dragon software package (http://www.talete.mi.it) to generate a 4884 physicochemical features of each molecule (including atom types, functional groups, topological, and geometric properties)
Estimating the perceptual ratings from chemical structure
To estimate the perceptual ratings from the chemical structure, we use a regularized linear model that is learned using elastic net regression^{24}. This model is trained on the DREAM dataset of 476 molecules. The input for the model consists of the chemoinformatic features of the molecules described above. Using these features, the model predicts the mean perceptual rating given by 49 subjects on each of the perceptual descriptors that we use above. Thus, for each molecule i, the chemoinformaticstoperception model learns a transformation C such that
where \(\widehat {\mathbf{p}}_{S,i}\) is the 19dimensional vector containing the model’s estimate of the mean ratings on the DREAM descriptors for the molecule i, and x_{S,i} is the 4884dimensional vector of molecule i’s chemoinformatic features.
Extending ratings to new descriptor lexicons
We define two tasks, direct and imputed. For the direct task, we have access to actual DREAM ratings for each test molecule. In the imputed task, we do not have access to the test molecule’s actual DREAM ratings. Instead, we begin by applying a previously trained and unpublished model used in the context of Keller et al.^{2} that can infer the ratings scores of any chemical on the DREAM verbal descriptors, given its chemoinformatic properties. For both tasks, the objective is to predict the test molecule’s Dravnieks ratings. Consequently, we also refer to the DREAM data as our source and the Dravnieks data as our target. We present three classes of model for each task, ratings, semantic, and mixed. Altogether the combination of the tasks and model classes results in six models, which we describe below.
As before, the real or imputed DREAM ratings scores for each molecule i can be collected into a 19dimensional perceptual vector p_{S,i}. In addition, for each DREAM descriptor d, we have a semantic vector s_{S,d}, which is a 300dimensional vector computed as described in the section describing the semantic vectors. We collect these into a source semantic matrix S_{S} of dimension 19 × 300 where again 19 is the number of DREAM perceptual descriptors.
We want to learn the ratings scores for any arbitrary set of descriptors—we call these our target descriptors. We assume that we can compute the semantic vectors corresponding to each of these perceptual descriptors d, denoted by s_{T,d}. Taking advantage of the structure inherent in these target semantic vectors is key to our method. We collect these into a target semantic matrix S_{T} of dimension D_{T} × 300 where D_{T} is the number of target descriptors. In the case of the results presented in the body of this paper, D_{T} = 131, because there are 131 Dravnieks descriptors that we use.
In this framework, our goal is to estimate the ratings scores for the target (Dravnieks) descriptors for each test molecule i, denoted by p_{T,i}.
In order to set a point for comparison, we propose a baseline model that takes the mean rating score for each targetset descriptor, across the training set of molecules for which ratings are available:
This is then used as the baseline estimate of the ratings scores across the targetset descriptors for a given new test molecule i. In the case where no training ratings are available for the target descriptors, we take the baseline to be the constant vector 0.
The first model class is composed of the semanticsonly models for the direct and imputed tasks (DirSem and ImpSem, respectively). These semanticsonly models assume that a distributional semantic space derived from a linguistic corpus shares structure with the olfactoryperceptual space in which perceptual ratings scores exist. Consequently, we seek to test whether we can leverage the structure of the semantic space to predict ratings in the perceptual ratings space. To learn the semanticsonly model S we proceed by supposing there exists a matrix S of dimension 19 × 131 that roughly maps from the semantic vectors for the source set of perceptual descriptors to the semantic vectors (collected into the matrix Σ_{S}) for the target set of perceptual descriptors (collected into the matrix Σ_{T}:
Our semanticsonly models make the assumption that S is also an appropriate transformation for mapping from the perceptual ratings for the source set of descriptors to the perceptual ratings for the target set for each molecule i:
In order to estimate S, we use elastic net regression. The regularization parameters are set by nested 10fold crossvalidation.
Note that of the three model types described in this section, the semanticsonly models are the only ones that do not rely on having access to any ratings scores for the source set (i.e., no p_{T} is required for training). However, to compare this model directly with the models that do use such information, we tested the effect of adding information about the mean rating to the model. Therefore, the final estimate for molecule i under this model, when target descriptors training molecules are available, would be:
The only difference between DirSem and ImpSem is in the nature of p_{S,i} and \(\overline {\mathbf{p}} _T\). Recall that in DirSem these are derived from real DREAM ratings data, while in ImpSem they are predictions of the chemoinformaticstoperception model.
The ratingsonly models DirRat and ImpRat rely on having access to ratings scores for the target descriptors, for some training set of molecules. They assumes that there is some function R that maps from ratings scores on the source descriptors to ratings scores on the target descriptors for each molecule i:
Once again, we estimate R using elastic net regression, with regularization weights set by nested 10fold crossvalidation. We also add information about the mean rating to the model, if available, so our final estimate under this model is:
For the mixed models direct and imputed we simply average the predictions of the semanticsonly and ratingsonly models:
In preliminary investigations, we also looked at other ways to combine the information in the semanticsonly and ratingsonly models, such as training a single regression model on the set union of the descriptors’ semantic vector values and molecule ratings, but a simple average performed best.
Evaluating performance
For each model, we vary the number of training molecules for which target descriptor ratings are available. We can then measure the median Pearson correlation between model M’s estimate \(\widehat {\mathbf{p}}_{T,i}\) and the ground truth p_{T,i} for each test molecule i as:
We use these correlations to assess whether the model’s performance differs significantly from the baseline model, by computing Zscores. For the SemanticsOnly model when we do not use any training molecules, the baseline is simply a correlation of zero, so the Zscore can be obtained using the Fisher rtoZ transformation:
However, for the other models, note that the correlation coefficient produced by model and the correlation coefficient produced by the Baseline model are not independent random variables. Thus, to determine whether these two correlations differ significantly, we must take their dependence into account, which the standard Fisher transformation does not do. Instead, we can use the method developed by ref.^{47}:
where
We can then compute the median of these Zscores for all molecules in the test set:
Permutation tests for evaluating smoothness in semantic prediction
We performed a permutation test by randomly permuting the semantic nearest neighbors of each descriptor, and then recomputing the correlation between the prediction performances (measured by Pearson’s correlation) of each point and of its permuted nearest neighbor. The resulting simulated correlations exceeded the true correlation of r = 0.4170 in 0 of the 10,000 permutations.
For each descriptor, the knearest neighbor (kNN) algorithm predicts the descriptor’s prediction performance (measured by Pearson’s r^{2}) by taking the distanceweighted average of the prediction performance of the knearest neighbors. The mean squared error of this algorithm is then computed, and the significance is evaluated using a permutation test. The permutation test is performed by randomly permuting the semantic nearest neighbors of each descriptor, and then recomputing the mean squared error of the resulting kNN predictions. The mean squared error of 2000 such permutations was never below that of the true mean squared error.
Tests for similarity between ratings and semantic vectors correlation matrices
To estimate the degree of structural similarity between the correlation matrix defined by Dravnieks and DREAM ratings (Fig. 2a), and that defined by the corresponding semantic vectors (Fig. 2b), we implemented two tests. In the first one, we computed the Procrustes dissimilarity between the rating matrix and the semantic matrix, and compared it against the expected dissimilarity between the original rating matrix and random permutation surrogates of the semantic matrix. A Wilcoxon test yields p < 0.05. For the second test, we found for each DREAM descriptor the Dravnieks descriptor with which it is maximally correlated, both in the ratings and semantic matrices. A Spearman test for the correlation between these two sequences yields r = 0.74, p < 10^{−4}. Conversely, the test for the maxima estimated along the Dravnieks descriptors yields r = 0.5, p < 10^{−9}.
Organization of semantic and rating spaces
The dendrogram in Fig. 4d was created by computing the cosine distance between the semantic vectors of the Dravnieks descriptors, fed into an agglomerative hierarchical cluster tree algorithm using the average over all element of a cluster to determine the distance between clusters (linkage function^{48}). The 2D projections in Fig. 2c and d were created using multidimensional scaling (mdscale function^{48}) with cosine distance for both maps. For the semantic organization, the fastText 300dimensional vectors corresponding to the DREAM descriptors were used; for the ratings organization, each descriptor was represented as vector of ratings over molecules.
Additional information on elastic net regression
LASSO and elastic net are regression algorithms that impose a regularization penalty on the regression weights in order to reduce model complexity and avoid overfitting.
For a regression model of the form
the regression weights in LASSO are estimated in order to minimize the following loss function:
where the first term is the squared error of the prediction, and the second term is a regularization penalty (a penalty on the regression weights), and λ_{1} is a regularization strength parameter. LASSO’s regularization penalty leads to a model that is sparse (i.e., produces few nonzero regression weights). This results in relatively more parsimonious and interpretable model. However, the LASSO loss function is not convex, so it does not produce a unique solution when the number of features is greater than the number of samples. When two features are highly correlated, LASSO will arbitrarily assign only one of the two features a nonzero weight, even if both contribute equally to the prediction in the ground truth model. This can lead to poor prediction performance.
Elastic net regression attempts to get around LASSO’s drawbacks. The regression weights are computed according to
where the first term is the squared error of the prediction, the second term is the L1 (or LASSO) regularization penalty, the third term is the L2 (or ridge regression) regularization penalty^{49}, and λ_{1} and λ_{2} are the corresponding regularization strengths. Elastic net regression seeks to combine the benefits of LASSO and ridge regression. Like LASSO, it results in a parsimonious, interpretable, sparse model where most of the regression coefficients are zero. However, like ridge regression, elastic net has a convex loss function and produces a unique solution even when the number of features is greater than the number of samples. Elastic net also overcomes the arbitrary feature selection drawback of LASSO. See^{49} for more details.
Sequentially selecting prototypical features
We now describe the technical details of the method used to create Fig. 3a. For a more thorough treatment please refer to ref.^{28}.
Let \({\cal X}\) be the space of all covariates from which we obtain the samples X^{(1)} and X^{(2)}; in our particular case, these will be the perceptual ratings over molecules. Consider a kernel function \(k:{\cal X} \times {\cal X} \to {\Bbb R}\) and its associated reproducing kernel Hilbert space (RKHS) \({\cal K}\) endowed with the inner product k(x_{i}, x_{j}) = 〈ϕ(x_{i}),ϕ(x_{j})〉 where \(\phi _{\mathbf{x}}({\mathbf{y}}) = k({\mathbf{x}},{\mathbf{y}}) \in {\cal K}\) is continuous linear functional satisfying ϕ_{x}:h \(\to\) h(x) = 〈ϕ_{x},h〉 for any function \(h \in {\cal K}:{\cal X} \to {\Bbb R}\).
The maximum mean discrepancy (MMD) is a measure of difference between two distributions p and q where if \({\mathbf{\mu }}_p = {\Bbb E}_{{\mathbf{x}}\sim p}[\phi _{\mathbf{x}}]\) it is given by:
Our goal is to approximate μ_{p} by a weighted combination of m subsamples Z ⊆ X^{(2)} drawn from the distribution q, i.e., \({\mathbf{\mu }}_p({\mathbf{x}}) \approx \mathop {\sum}\limits_{j:{\mathbf{z}}_j \in Z} w_jk({\mathbf{z}}_j,{\mathbf{x}})\) where w_{j} is the associated weight of the sample z_{j}∈X^{(2)}. We thus need to choose the prototype set Z ⊆ X^{(2)} of cardinality (.) m where n^{(1)} = X^{(1)} and learn the weights w_{j} that minimizes the finite sample MMD metric with the additional nonnegativity constraint for interpretability, as given below:
Index the elements in X^{(2)} from 1 to n^{(2)} = X^{(2)} and for any Z ⊆ X^{(2)} let L_{Z} ⊆ [n^{(2)}] = {1, 2,…,n^{(2)}} be the set containing its indices. Discarding the constant terms in (16) that do not depend on Z and w we define the function
where K_{i,j} = k(y_{i}, y_{j}) and \(\mu _{p,j} = \frac{1}{{n^{(1)}}}\mathop {\sum}\limits_{{\mathbf{x}}_i \in X^{(1)}} k({\mathbf{x}}_i,{\mathbf{y}}_j);\forall {\mathbf{y}}_j \in X^{(2)}\) is the pointwise empirical evaluation of the mean μ_{p}. Our goal then is to find an index set L_{Z} with L_{Z} ≤ m and a corresponding w such that the set function \(f:2^{\left[ {n^{(2)}} \right]} \to {\Bbb R}^ +\) defined as
is maximized. Here supp(w) = {j:w_{j} > 0}. We will denote \({\mathbf{\zeta }}^{\left( {L_Z} \right)}\) the maximizer of set L_{Z}.
The above problem is NPhard to solve. The ProtoDash algorithm, however, efficiently solves this problem and is shown to have a tight approximation guarantee^{28}. If Q denotes the 476 × 19^{24} perceptual matrix then we set X^{(1)} = X^{(2)} = Q^{T} and run the following algorithm.
The order in which elements are added to L is the order depicted in Fig. 3a.
Predictions of paradigm odors for molecular families
We extracted every term used to describe the paradigm odors for any of the 35 molecules in 4 families: 9 molecules from the family of alkyl aldehydes, 9 molecules from primary alcohols, 8 molecules from 2ketones, and 9 molecules from carboxylic acids, that appeared in The Good Scents Company and Perfumer and Flavorist libraries. We included 80 descriptors used to describe all the 35 molecules, ignoring instances where the term was only weakly associated–e.g., fruity nuance or weak hint of apple. We then predicted for these 35 molecules the 19 DREAM perceptual descriptors from the Dragon molecular descriptors of the molecules and then used the Semantic model to obtain ratings for the 80 terms. Besides the AUC, we also computed for each molecule a pvalue by performing a onesided ttest for the difference between the means of the predictions for the terms that were used to describe the molecule and the terms that were not used to describe the molecule. A Kolmogorov–Smirnov test on these pvalues reveals that they are not uniformly distributed (p < 1e–6), hence, overall predicted ratings for descriptors that are used to for a molecule rank much higher than the ones that are not.
Data availability
All relevant data are available from the authors. The code to predict DREAM descriptors is available here: https://github.ibm.com/adhuran/Olfaction The code to predict Dravnieks descriptors is available here: https://github.com/edg2103/odormatic.
References
Wise, P. M., Olsson, M. J. & Cain, W. S. Quantification of odor quality. Chem. Senses 25, 429–443 (2000).
Kaeppler, K. & Mueller, F. Odor classification: a review of factors influencing perceptionbased odor arrangements. Chem. Senses 38, 189–209 (2013).
Noble, A. C. et al. Progress towards a standardized system of wine aroma terminology. Am. J. Enol. Vitic. 35, 107–109 (1984).
Lawless, L. J. & Civille, G. V. Developing lexicons: a review. J. Sens. Stud. 28, 270–281 (2013).
Amoore, J. E. Specific anosmia: a clue to the olfactory code. Nature 214, 1095 (1967).
Iatropoulos, G. et al. The language of smell: connecting linguistic and psychophysical properties of odor descriptors. Cognition 178, 37–49 (2018).
Bushdid, C., Magnasco, M. O., Vosshall, L. B. & Keller, A. Humans can discriminate more than 1 trillion olfactory stimuli. Science 343, 1370–1372 (2014).
Yeshurun, Y. & Sobel, N. An odor is not worth a thousand words: from multidimensional odors to unidimensional odor objects. Annu. Rev. Psychol. 61, 219–241 (2010).
Schab, F. & Crowder, R. in Memory for Odors (eds Crowder, R. & Schab, F.) 71–91 (Lawrence Erlbaum Associates, Hillsdale, NJ, 1995).
Engen, T. Remembering odors and their names. Am. Sci. 75, 497–503 (1987).
Cain, W. To know with the nose: keys to odor identification. Science 343, 1370–1372 (1979).
Larsson, M. Semantic factors in episodic recognition of common odors in early and late adulthood: a review. Chem. Senses 22, 623–633 (1997).
Dravnieks, A. Odor quality: semantically generated multidimensional profiles are stable. Science 218, 799 (1982). –80.
Kuhl, P. Early language acquisition: cracking the speech code. Nat. Rev. Neurosci. 5, 831–843 (2004).
Regier, T. & Kay, P. Language, thought, and color: Whorf was half right. Trends Cogn. Sci. 13, 439–446 (2009).
Grusser, O. & Landis, T. Visual Agnosias and Other Disturbances of Visual Perception and Cognition (Macmillan Press, London, 1991).
Meteyard, L., Bahrami, B. & Vigliocco, G. Motion detection and motion verbs: language affects lowlevel visual perception. Psychol. Sci. 18, 1007–1013 (2007).
Lupyan, G. & Ward, E. J. Language can boost otherwise unseen objects into visual awareness. Proc. Natl Acad. Sci. USA 110, 14196–14201 (2013).
Gottfried, J. A. & Dolan, R. J. The nose smells what the eye sees: crossmodal visual facilitation of human olfactory perception. Neuron 39, 375–386 (2003).
Kiela, D., Bulat, L. & Clark, S. Grounding semantics in olfactory perception. Assoc. Comput. Linguist. 231–326 (2015).
Nozaki, Y. & Nakamoto, T. Predictive modeling for odor character of a chemical using machine learning combined with natural language processing. PLoS ONE 13, e0198475.
Palatucci, M., Pomerleau, D., Hinton, G. E. & Mitchell, T. M. Zeroshot learning with semantic output codes. Adv. Neural Inform. Process. Syst. 1410–1418 (2009).
Dravnieks, A. Atlas of odor character profiles. ASTM data series.ASTM Committee E18 on Sensory Evaluation of Materials and Products (ASTM, Philadelphia, PA, 1985).
Keller, A. et al. Predicting human olfactory perception from chemical features of odor molecules. Science 355, 820–826 (2017).
Harris, Z. Distributional structure. Word 10, 146–162 (1954).
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. Adv. Neural Inform. Process. Syst. 3111–3119 (2013).
Schwarz, N. Selfreports: How the questions shape the answers. Am. Psychol. 54, 93–105 (1999).
Gurumoorthy, K., Dhurandhar, A. & Cecchi, G. Protodash: fast interpretable prototype selection. Preprint at https://arxiv.org/abs/1707.01212v2 (2017).
Budanitsky, A. & Hirst, G. Evaluating wordnetbased measures of lexical semantic relatedness. Comput. Linguist. 32, 13–47 (2006).
Ohloff, G. & Pickenhagen, K. P. Scent and Chemistry (Wiley, Hoboken, 2012).
Rubenstein, H. & Goodenough, J. B. Contextual correlates of synonymy. Commun. ACM 8, 627–633 (1965).
McDonald, S. Environmental determinants of lexical processing effort. Ph.D. thesis, University of Edinburgh (2000).
Landauer, T. & Dumais, S. A solution to Platoś problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychol. Rev. 104, 211–240 (1997).
Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).
McNamara, D. S., Kintsch, E., Songer, N. B. & Kintsch, W. Are good texts always better? text coherence, background knowledge, and levels of understanding in learning from text. Cogn. Instr. 14, 1–43 (1996).
Foltz, P. W., Kintsch, W. & Landauer, T. K. The measurement of textual coherence with latent semantic analysis. Discourse Process. 25, 285–307 (1998).
Lund, K. & Burgess, C. Producing highdimensional semantic spaces from lexical cooccurrence data. Behav. Res. Methods, Instrum., Comput. 28, 203–208 (1996).
Dumais, S. Datadriven approaches to information access. Cogn. Sci. 27, 491–524 (2003).
Devanand, D. P. et al. Olfactory deficits in patients with mild cognitive impairment predict alzheimer?s disease at followup. Am. J. Psychiatry 157, 1399–1405 (2000).
Corcoran, C. et al. Olfactory deficits, cognition and negative symptoms in early onset psychosis. Schizophr. Res. 80, 283–293 (2005).
Joerges, J., Küttner, A., Galizia, C. G. & Menzel, R. Representations of odours and odour mixtures visualized in the honeybee brain. Nature 387, 285 (1997).
Medjkoune, M. et al. Towards a nonoriented approach for the evaluation of odor quality. In International Conference on Information Processing and Management of Uncertainty in KnowledgeBased Systems, 238–249 (Springer, Switzerland, 2016).
Keller, A. & Vosshall, L. B. Olfactory perception of chemically diverse molecules. BMC Neurosci. 175, 5 (2016).
Bojanowski, P., Grave, E., Joulin, A. & Mikolov, T. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016).
Wittgenstein, L. Philosophical Investigations (Blackwell, Oxford, 1953).
Deerwester, S., Dumais, S. T. & Harshman, R. Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990).
Steiger, J. H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).
MATLAB. Version 9.1.0 (R2016b) (The MathWorks Inc., Natick, MA, 2016).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).
Acknowledgements
We would like to thank Rick Gerkin and Pablo Polosecki for reading the manuscript and providing useful comments. Smell Icon used was adapted from Taka Oumehara from https://thenounproject.com and book icon from Zlatko Najdenovski from www.flaticon.com.
Author information
Authors and Affiliations
Contributions
E.D.G. developed the predictions and semantic model, A.D. developed the chemoinformatic model. All authors together interpreted the results, approved the design of the figures and the text, which were prepared by E.D.G., G.A.C., and P.M.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gutiérrez, E.D., Dhurandhar, A., Keller, A. et al. Predicting natural language descriptions of monomolecular odorants. Nat Commun 9, 4979 (2018). https://doi.org/10.1038/s41467018074399
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467018074399
This article is cited by

OWSum: algorithmic odor prediction and insight into structureodor relationships
Journal of Cheminformatics (2023)

Textbased predictions of COVID19 diagnosis from selfreported chemosensory descriptions
Communications Medicine (2023)

Olfactory perception prediction model inspired by olfactory lateral inhibition and deep feature combination
Applied Intelligence (2023)

Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules
Scientific Reports (2022)

Application of artificial intelligence to decode the relationships between smell, olfactory receptors and small molecules
Scientific Reports (2022)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.