Chemical features mining provides new descriptive structure-odor relationships
Around the turn of the century , with its acknowledgement as an object of science by the Nobel society 1 the hidden sense associated with the perception of odorant chemicals , hitherto considered superfluous to cognition , became a focus of study in its own right ., Odors are emitted by food , which is a source of pleasure 2; they also influence our relations with others 3 ., The olfactory percept encoded in odorant chemicals contributes to our emotional balance and wellbeing: olfactory impairment jeopardizes this equilibrium 4 , 5 ., Neuroscientific studies have revealed that odor perception is the consequence of a complex phenomenon rooted in the chemical properties of a volatile molecule ( described by multiple physicochemical descriptors ) further detected by our olfactory receptors in the nasal cavity 6 ., A neural signal is then transmitted to central olfactory brain structures 7 ., At this stage , a complete neural representation , called “odor” is generated and then , it can be described semantically by various types of perceptual qualities ( e . g . , musky , fruity , floral , woody etc . ) ., While it is generally agreed that the physicochemical characteristics of odorants affect the olfactory percept , no simple and/or universal rule governing this Structure Odor Relationship ( SOR ) has yet been identified ., Why does one odorant smell of rose and another smell of lemon ?, Given the fact that the totality of the odorant message was encoded within the chemical structure , chemists have tried for a long time to identify relationships between chemical properties and odors ., Topological descriptors , eventually associated with electronic properties or molecular flexibility , have been tentatively connected to odorant descriptors ., For instance , molecules carrying a sulfur atom and/or having low molecular weight or low structural complexity are often rated as unpleasant 8–10 ., In addition to the hedonic valence of odors , others have looked for predictive models describing odor perception and quality ( see 11–14 ) ., Indeed , this was the aim of a crowd-sourced challenge recently proposed by IBM Research and Sage called DREAM Olfaction Prediction Challenge ., The challenge resulted in several models that were able to predict pleasantness and intensity as well as 8 out of 19 semantic descriptors ( namely “garlic” , “fish” , “sweet” , “fruit” , “burnt” , “spices” , “flower” and “sour” ) with an average correlation of predictions across all models above 0 . 5 15 ., Although these investigations brought evidence that chemical features of odorants can be linked to odor perception , the stimulus-percept problem raised a number of issues ., For instance , the stimulus-percept relationship is generally viewed as bijective in that one physicochemical rule describes or predicts one quality ., However , some cases suggest the existence of more than a single rule to relate chemistry and perception ., Indeed , chemicals belonging to different families can trigger a “camphor” or a musky smell 16 ., On the other hand , a single chiral center can render a compound odorless or shift its perceived odor completely , as is the case for ( + ) and ( - ) -carvone 17 ., These examples strengthen the notion that the connections between the chemical space and the perceptual space are subtler than previously thought with multiple physicochemical rules describing a given quality ., At best , the bijective SOR rules may be only be applicable to a very small fraction of the chemical space , with the remaining part of the perceptual space being best described using a multiple rules approach ., The complexity of available databases , they include both thousands of chemical properties and a large heterogeneity in perceptual descriptions , 18–21 means that the manual generation of multiple rules is not feasible ., In other words , to better understand the stimulus-percept issue in olfaction , there is a clear need to extract knowledge automatically and in an intelligible manner ., Such an approach is positioned upstream of predictive modeling since it will enable modeling that extracts descriptive rules from the data that link subgroups belonging to both chemical and perceptual spaces ., The main aim of our study was to develop such a computational framework to discover new descriptive structure-odor relationships ., To achieve this , we first set up a large database containing more than 1600 odorant molecules described by both physicochemical properties and olfactory qualities ., We then developed an original methodology based on the discovery of physicochemical descriptions distinguishing between a group of objects given a target or class label , namely odor qualities ., This approach has been widely studied in Artificial Intelligence ( AI ) , data mining and machine learning ., Specifically , supervised descriptive rules were formalized through subgroup discovery , emerging pattern/contrast-sets mining 22 ., In all cases , we face a set of objects associated with descriptions and these objects are related to one or several class labels ., This new pattern mining method , a variant of redescription mining 23 , allows the discovery of pairs consisting of a description ( of physicochemical properties ) and a label ( or sub-set of labels , olfactory qualities ) ., The strength of the rule ( SOR in our application ) is evaluated through a new quality-control measure detailed in the Methods section ., We designed and set up a database describing odorant molecules by both their perceptual and physicochemical properties ., Here , data from different sources were extracted and grouped:, ( i ) for odorant identification and olfactory qualities , we referred respectively to the PubChem website ( https://pubchem . ncbi . nlm . nih . gov/ ) and the textbook by Arctander 24;, ( ii ) for physicochemical properties , we referred to the Dragon software package ( http://www . talete . mi . it/index . htm ) ., Olfactory qualities were thus gathered from the book “Perfume and Flavor Chemicals” , published in 1969 by Steffen Arctander ., In this book , Arctander gives a complete description , including olfactory and trigeminal qualities as well as flavors , of 3102 odorants ( detailed physicochemical properties of 1689 odorants among these 3102 odorants were retrieved , see below ) ., These odorants were further identified by chemical name , molecular weight and corresponding olfactory qualities ., Here , the 74 olfactory qualities selected by Chastrette and colleagues 25 were used as a reference list ., These qualities were selected in a study of the whole of Arctander’s book by excluding those that did not provide qualitative olfactory information and those that were the least frequent ., Note that before selecting this source , we ran a comparison with other existing Atlases and websites used for research , teaching and applicative purposes: specifically , the Dravnieks Atlas 26 , the Boelens Atlas ( see 27 ) , and the Flavornet website ( http://www . flavornet . org ) ., These sources ( atlases , book and website ) were compared along a series of parameters ( the comparison took into account all odorants for which we collected CID numbers ) ., The first parameter of interest was the number of molecules studied in the source , and was respectively 1689 , 138 , 263 , and 660 for the Arctander , the Dravnieks , the Boelens and the Flavornet ( here , only molecules for which we found a PubChem Compound Identification or CID are taken into account ) ., The second parameter was the number of evaluators ( and their expertise level ) who smelled the compounds and provided the olfactory qualities: one trained evaluator for the Arctander , a large panel of evaluators for the Dravnieks ( although there seems to be a large heterogeneity in the expert profile of these panelists , and little information as to the extent of training that panelists were given ) , six trained evaluators for the Boelens , and no information is given regarding the panelists for the Flavornet website ., Third , when considering the way olfactory qualities were collected in the source , both the Arctander and the Flavornet used a binary format ( presence/absence of quality ) , and both the Dravnieks and the Boelens used a scale of intensity or agreement ., Fourth , we compared the number of olfactory qualities used in each atlas/book/website and observed the following distribution ( the average number of qualities per molecule is in brackets ) : 74 ( 2 . 88 ) for the Arctander , 146 ( 29 . 99 ) for the Dravnieks , 30 ( 12 . 86 ) for the Boelens , and 197 ( 2 . 72 ) for the Flavornet ., Note also that the minimum ( and the maximum ) number of qualities for one molecule was: Arctander ( min: 1; max: 10 ) , Dravnieks ( min: 5; max: 52 ) , Boelens ( min: 0; max: 22 ) , Flavornet ( min: 1; max: 5 ) ., Thus , this analysis showed that whereas some sources are characterized by a large number of molecules ( e . g . Arctander and Flavornet ) , others contain only a limited number of odorants ( e . g . Boelens and Dravnieks ) ., Moreover , there is great heterogeneity between these different sources with regards to the number and the degree of expertise of the evaluators ., Some sources involve a large number of evaluators but with heterogeneous profiles ( e . g . Dravnieks ) and others involve a limited number of experts ( e . g . Boelens and Arctander ) ., Finally , whereas some sources have , on average , between 10 and 30 qualities per odorant ( e . g . Boelens and Dravnieks ) , the average number is around three for others ( e . g . Arctander and Flavornet ) ., In view of these parameters , and because the descriptive approach used in this study requires a large database , we used the Arctander book because it contained the highest number of odorant molecules ( 1689 ) and a reasonable number of qualities per odorant ( 2 . 88 on average ) ., Odorant physicochemical properties were then obtained using Dragon , a software application that enables the calculation of 4885 molecular descriptors ( Talete ) ., Descriptors included in our dataset ranged from the simplest atom types , functional groups and fragment counts , to topological and geometrical descriptors ., As Dragon requires 3D structure files , these were collected from the PubChem website ( https://pubchem . ncbi . nlm . nih . gov ) by using the compound identifier number of each odorant ( CID ) ., Individual odorant CIDs were obtained by using the CAS Registry Number and/or the chemical name of the odorant as an entry in the PubChem website ., In total , 1689 CIDs were found for the 3102 odorants ., In the following section , we study the set M of odorant molecules that are described by n physicochemical properties denoted F . Each property fi ∈ F is a function that associates a real value with a molecule: fi: M → image ( fi ) with image ( fi ) an interval of R . The olfactory qualities are denoted by O and class is a mapping that associates a subset of O to a molecule: class: M → 2O ., Here , we developed an original subgroup discovery approach to mine descriptive rules that specifically characterize subsets of olfactory qualities ( O ) ., The specificity of this approach is intended to be able to extract rules with several olfactory qualities as targets , and also to treat unbalanced classes robustly , i . e . , the fact that some olfactory qualities are very rare ( e . g . “musty” ) compared to others ( e . g . “fruity” ) ., Subgroup discovery is a generic data mining method aimed at discovering regions in the data that stand out with respect to a given target ., We instantiated this framework in order to identify the conditions on some odorant physicochemical properties that are strongly associated with olfactory qualities ., A structure odor rule ( SORule ) , denoted D → Q , is defined by a physico- chemical description D and a set of olfactory qualities Q ⊆ O . The description is a set of n intervals D = ⟨x1 , y1 , x2 , y2 , … , xn , yn⟩ , each being a restriction on the value image of its corresponding physicochemical property: xi , yi ⊆ image ( fi ) ., The molecules whose values on physicochemical descriptors belong to the intervals of the description D are members of the coverage of D:, coverage ( D ) ={m∈M∀i=1…n , xi≤fi ( m ) ≤yi}, We count the number of molecules in the coverage with support ( D ) = |coverage ( D ) | ., The quality of a rule is evaluated with respect to the olfactory qualities of the molecules in its coverage ., First , the precision measure gives the proportion of the molecules of the coverage of D that also have ( part of ) the olfactory qualities Q:, P ( D→Q ) =|{m∈coverage ( D ) class ( m ) ⊆Q}|support ( D ), This is the percentage of times the rule is triggered for molecules whose qualities are in Q . On the other hand , it is also important to know if the rule covers all the molecules of quality Q . This is what the recall measure evaluates:, R ( D→Q ) =|{m∈coverage ( D ) class ( m ) ⊆Q}||{m∈Mclass ( m ) ⊆Q}|, These two measures behave in opposite ways: when one increases , the other decreases ., One way to globally evaluate a rule is to use the F1 measure , the harmonic mean between the precision and recall measures:, F1 ( D→Q ) =2P ( D→Q ) R ( D→Q ) P ( D→Q ) +R ( D→Q ), As mentioned above , the olfactory qualities are more or less frequent in the data ., To take that into account , the Fβ measure gives more importance to the precision measure for rare olfactory qualities , while favoring the recall measure for frequent qualities:, Fβ ( D→Q ) = ( 1+β ( support ( Q ) ) P ( D→Q ) R ( D→Q ) β ( support ( Q ) ) P ( D→Q ) +R ( D→Q ), with support ( Q ) = |{m ∈ M |class ( m ) ⊆ Q}| and, β ( x ) = ( 0 . 5× ( 1+tanh ( xβ-xlβ ) ) ) 2, Here , the terms xBeta and lBeta are determinant in choosing the appropriate sigmoid model , and are values that can be set by the experimenter ., Given that , our approach aims to discover rules D → Q whose support support ( D ) is greater than a threshold minSupp and with |Q| is lower or equal to a value maxQual ., Those parameters make it possible to identify rules that are supported by sufficient odorant molecules , and also that are specific to a small set of olfactory qualities ., The maxQual parameter enforces that the right-hand side of the rule contains a limited number of olfactory qualities to be interpretable by the analyst ., Similarly , a maxProp parameter allows to limit the number of ( physicochemical ) conditions in the left-hand side of the rules ., To illustrate the previous definitions , let us consider the toy olfactory dataset given in Table 1 ., This dataset contains 6 molecules identified by their IDs M = {1 , 2 , 3 , 4 , 5 , 6} ., Each molecule is described by its molecular weight MW , its number of atoms nAt and its number of carbon atoms nC , that is , F = {MW , nAt , nC} ., Besides , the molecules are also associated with their olfactory qualities among O = {fruity , vanillin , woody} ., Let us consider the description, D=⟨128 , 151 , 23 , 29 , 9 , 12⟩, Its coverage is coverage ( D ) = {2 , 3 , 5 , 6} ., If we consider the odorant quality Q = {vanillin} , as there is 2 molecules of coverage ( D ) with this quality , the precision of the rule is equal to:, P ( D→Q ) =24, As there are 3 molecules in the whole dataset with that quality , the recall of the rule is:, R ( D→Q ) =23, Its F1 measure is thus equal to:, F1 ( D→Q ) =227, Detailed information regarding the principle of the algorithm are provided as S1 Text ., Our olfactory dataset includes 1689 molecules described by 74 olfactory qualities ., The dataset is multi-labeled , each molecule being associated with one or several olfactory qualities ., On average , each molecule refers to 2 . 88 olfactory qualities among the 74 possible labels ., Moreover , the frequency of olfactory qualities across odorants is unbalanced: on average a quality is used in 65 . 79 molecules ( standard deviation: 105 . 28 ) , the maximum is reached for the “fruity” quality ( used in 570 molecules ) , the minimum for musty ( used in only 2 molecules ) ., Fig 1 illustrates the entire building process of the database ., Fig 2 presents a world cloud of the 74 olfactory qualities ., With regard to the physicochemical properties , our original database contained more than 4000 physicochemical features ., For the purpose of a rational approach where features can be interpreted on a chemical basis , we selected attributes that were relevant , but more importantly easily interpretable ., This approach is strongly inspired by the so-called 3D-olfactophore , where such easily interpretable features computed on odorants sharing the same olfactory percept are gathered in the 3 dimensions of space ., Such features are typically Hydrogen bond donor/acceptor , Aromatic cycle , Charged atom , etc ., This methodology is typically useful for molecular scientists to learn about structure-property relationships and design new molecules which fulfill the properties of these olfactophores 28 ., Here the features we used were a series of physico-chemical properties ., Thus , we selected constitutional , topological and chemical descriptors that represent molecular features which can be easily interpreted and extrapolated for further predictive models ., They include the following categories: constitutional indices ( n = 29; ex . “Molecular weight” ) , ring descriptors ( n = 7; ex . “Number of rings” ) , functional group counts ( n = 40; ex . “Number of esters” ) , molecular properties ( n = 6; ex . “Topological polar surface area” ) ., To select these descriptors , we screened the whole set of descriptors proposed by Dragon ., We carefully selected descriptors able to provide information interpretable by any molecular scientist ., The cost of selecting interpretable descriptors is a reduction in the description of the dataset ., To evaluate the loss of information on the variance of a given molecular dataset , descriptors were computed on a set of 2620 odorants provided by Saito and colleagues 29 ., Finally , 347 descriptors remained after filtering the following: correlated ( above 0 . 85 ) , constant for the whole dataset ( no variation across parameters ) , not available for the whole dataset ., After the dimensionality reduction , our selected 82 descriptors accounted for 37 . 2% of the original variance ., When choosing randomly 82 descriptors within this set of 347 , the variance always falls below 25% , suggesting that our descriptors performed quite well at describing a molecular set with a certain degree of variability ., Finally , when projecting the entire set of molecules on to the two first components of a PCA , the dataset remains well split and molecules were still distinguishable ., First , the physicochemical rules were generated for each of the 74 qualities based on the 82 descriptors ., This was done using the following parameters: maxoutput ( 100 ) , beamwidth ( 30 ) , MaxQual ( 1 ) , MaxProperties ( 8 ) , max Supp ( 700 ) , XBeta ( 110 ) , IBeta ( 20 ) , and four different minSupp ( 5 , 10 , 20 and 30 ) ( see Methods section and S1 Text for a detailed definition of these parameters ) ., Second , an algorithm search for the best rules or combination of rules ( with a maximum of 12 rules ) for each of the 74 qualities and the four different minSupp ( from 5 to 30 ) ., At this stage , the rules or combination of rules were ranked as a function of their Precision ., Here , to evaluate the best rule or combination of rules that can describe each quality , we calculated for each rule ( or combination of rules ) the distance ( Euclidian ) from the “ideal” situation defined as the data-point with an error of “0” ( error was calculated as one minus precision ) and the best recall ( value of 1 in the y-axis , meaning that all molecules that belong to the quality are described by these physicochemical rules ) ., The point ( s ) with the smallest distance was ( were ) selected as the best rule or combination of rules for a given quality ., From this selection , we built a list of rules and/or combination of rules for each quality ( see S1 Table ) ., We showed that around 90% of the olfactory qualities were described by 1 to 6 rules and 66% ( 49 qualities among 74 ) were described by 3 , 4 or 5 rules ( see Fig 3a ) ., Moreover , for the same quality , different rules or combinations of rules were selected because their distance to the “ideal” situation ( recall: 1; error:, 0 ) was the same ( see an example in Fig 3b ) ., Fig 3c shows an example of the chemical structure of the molecules described by the same quality ( jasmine here ) and rules/combinations of rules ., To compare olfactory qualities according to their description by physicochemical rules , we plotted all physicochemical rules ( and/or combination of rules ) of each quality in a 2D space comprising error ( x-axis ) and recall ( y-axis ) ( Fig 4 ) ., As can be seen , whereas some qualities were close to the “ideal” situation others were very far ., First , 38 qualities ( 51 . 35% , named “Group 1” ) exhibited an error rate lower than 0 . 5 and a recall greater than ( or equal to ) 0 . 5 ( sulfuraceous , vanillin , phenolic , musk , sandalwood , almond , orange-blossom , jasmine , hay , tarry , smoky , lilac , piney , camphor , grape , anisic , buttery , gassy , fatty , waxy , acid , minty , aromatic , mossy , violet , citrus , peppery , caramelic , medicinal , tobacco , pear , lily , sour , orange , animal , honey , hyacinth , rose ) ., Second , 17 qualities ( 22 . 97% , named “Group 2” ) exhibited an error rate lower than 0 . 5 but a recall lower than 0 . 5 ( amber , geranium , metallic , fruity , pineapple , ethereal , plum , woody , balsamic , creamy , green , berry , oily , spicy , floral , winey , herbaceous ) ., Third , 18 qualities ( 24 . 32% , named “Group 3” ) showed an error rate greater than ( or equal to ) 0 . 5 and a recall greater than ( or equal to ) 0 . 5 ( leathery , aldehydic , mushroom , coco , mimosa , tea , nut , root , peachy , earthy , powdery , orris , apple , leafy , apricot , musty , brandy , narcissus ) ., Fourth , one quality ( 1 . 35% , named “Group 4” ) showed an error rate greater than ( or equal to ) 0 . 5 and a recall lower than 0 . 5 ( banana ) ., To further examine whether the generated physicochemical rules were specific to a given perceptual quality , in other words whether they provided a good and relevant model , we used Bootstrap confidence intervals to evaluate whether the generated F-measure of the rules/models was significative ., Here , knowing that a given set of rules covers X molecules , we sampled 100 , 000 sets of X molecules ( with replacement ) and calculated the F-measure of each sample according to the studied quality ., Next , the confidence intervals ( CI: 99% ) of these sets were computed ., Afterwards , the F-measure of the set of discovered rules was compared to this CI ., Results showed that for all 74 qualities , the F-measure was significant in that its value was outside ( and greater ) the CI at 99% ., Finally , to examine how the model built with 82 physicochemical descriptors performed compared to a model built with all 4000 descriptors , we calculated the F-measure for each quality ( computed on the basis of all sets of rules ) in both types of models ., Results showed that , on average , the F-measure was significantly greater ( p<0 . 0001 ) in the model with 82 physicochemical descriptors ( mean = 0 . 592 , SEM = 0 . 012 ) compared to the model with all 4000 descriptors ( mean = 0 . 487 , SEM = 0 . 011 ) , reflecting that the use of a small but explicative and intelligible set of descriptors enhances performance ., To sum up , we provide here a computational framework that enables the automatic extraction , from a complex and heterogeneous dataset , descriptive rules linking subgroups in a chemical space onto subgroups in a perceptual space ., As can be seen in Fig 3a , only 3 qualities could be best described by a single physicochemical rule whereas more than two thirds of the qualities needed between 3 and 5 rules to be described ., When dealing with the confidence of the rules , a gradient was observed whereby some rules were associated with a good rate of recall and minimum rate of error , whereas other rules exhibited a lower confidence in describing olfactory qualities ., Note that all the generated rules are available to the reader in S1 Table ., The computational approach that we developed is available at the following address: https://projet . liris . cnrs . fr/olfamine/ Here , we analyzed some of the best-known qualities in the field of olfactory evaluation , namely fruity , floral , woody , camphor , earthy , spicy , fatty ., The analysis of the rules and combinations of rules ( see S1 Table ) , shows that the number of rules is quite high for these qualities ranging from six ( floral ) , seven ( camphor , earthy ) , eight ( spicy , woody ) , nine ( fatty ) to twelve ( fruity ) ., From a physicochemical point of view , translated into interpretable rules , the floral quality is characterized by either aromatic and strongly hydrophobic molecules or non-aromatic and moderately hydrophobic odorants ., For camphor , molecules are rather small in size , moderately hydrophobic , and eventually cyclic ., The earthy quality is characterized by moderately hydrophobic molecules with unsaturations ., The spicy quality is characterized by rather rigid molecules , eventually aromatic ., Woody quality includes hydrophobic molecules , rather not cyclic nor aromatic ., For the fatty , the molecules have a larger carbon-chain skeleton which is highly hydropobic with aldehyde or acid functions ., Finally , for the fruity quality , molecules are described as having moderate hydrophobicity and being medium to large in size ., To push the interpretation further , we examined qualities associated with generated physicochemical rules with the highest level of confidence ., Here , we attempted, ( i ) to understand the rules based on a priori knowledge and, ( ii ) to examine whether the rules could raise new scientific assumptions ., We analyzed a total of eleven qualities corresponding to the first quartile of the distribution of all rules ., Based on the Euclidian distance to the “ideal” situation; 473 rules were generated by our analysis ( see Fig 4 ) ., These qualities were: sulfuraceous , vanillin , phenolic , musk , sandalwood , almond , orange-blossom , jasmine , hay , tarry , smoky ., The “sulfuraceous” quality was described as follows: R1: 0 . 0<nCsp2<0 . 0 0 . 0<nHAcc<0 . 0 11 . 611<Se<22 . 069 144 . 039<SAtot<222 . 269 0 . 0<TPSA ( Tot ) <50 . 6; R2: 1 . 0<nS<2 . 0 1 . 0<nC<6 . 0 0 . 0<N%<0 . 0 25 . 0<C%<33 . 3 38 . 8<TPSA ( Tot ) <64 . 18; R3: 1 . 0<nS<2 . 0 -0 . 264<Hy<0 . 323 102 . 715<SAtot<222 . 269 0 . 0<O%<6 . 3 ., These descriptions suggest , somewhat intuitively , that sulfuraceous odorants encompass molecules with one or two sulfur atoms and are moderately heavy , with a maximum of six carbon atoms ., Four rules defined the “phenolic” quality: R1: 216 . 155<SAtot<218 . 661 0 . 0<nCrs<0 . 0 0 . 0<nOHp<0 . 0 30 . 4<C%<45 . 0 0 . 0<Ui<2 . 322; R2: 1 . 117<Mi<1 . 118 -0 . 768<Hy<-0 . 158 0 . 0<nR = Ct<0 . 0 43 . 5<H%<50 . 0 0 . 0<nOxiranes<0 . 0 0 . 0<nR = Cp<0 . 0; R3: 2 . 807<Uc<2 . 807 3 . 0<nCp<5 . 0 0 . 4<ARR<0 . 545 2 . 0<Ui<2 . 0 -0 . 888<Hy<-0 . 277 37 . 8<C%<40 . 0 0 . 0<nOHt<0 . 0 0 . 0<nOHp<0 . 0; R4: 0 . 6<ARR<0 . 75 1 . 0<nArOH<2 . 0 2 . 807<Uc<3 . 17 170 . 356<SAtot<222 . 475 0 . 893<MLOGP<2 . 778 0 . 0<nArCO<0 . 0 ., Thus , odorants having a “phenolic” quality are of moderate size , with few unsaturations and low hydrophilicity ( and high lipophilicity ) ., It can be regarded as a cyclic molecule ., A good consistency is observed between the 4 rules ., For “vanillin” , the following rules were observed: R1: 0 . 5<ARR<0 . 545 3 . 0<nCb-<4 . 0 3 . 0<nHAcc<3 . 0 1 . 0<nArOR<2 . 0 0 . 0<nR = Cp<0 . 0 0 . 0<nArCO<0 . 0 38 . 1<C%<46 . 2; R2: 3 . 0<nCb-<3 . 0 3 . 0<nO<3 . 0 0 . 0<nArCOOR<0 . 0 -0 . 727<Hy<0 . 66 42 . 1<H%<50 . 0 0 . 0<nArCO<0 . 0 38 . 1<C%<42 . 3; R3: 2 . 0<nCsp3<2 . 0 1 . 0<nArOR<2 . 0 0 . 699<MLOGP<1 . 75 0 . 0<nArCOOR<0 . 0 0 . 0<nArCO<0 . 0 2 . 0<nCb-<4 . 0 ., These descriptions suggest that odorants belonging to this group are mostly cyclic molecule ( like the prototypical molecule vanillin ) , with 3 Hydrogen bond acceptors branched on saturated carbons atoms on an aromatic cycle ., When considering the “musk” quality , the following rules emerged: R1: 3 . 72<MLOGP<4 . 045 2 . 0<nCrs<15 . 0 1 . 0<nCIC<1 . 0 333 . 936<SAtot<436 . 545; R2: 4 . 0<nCb-<6 . 0 33 . 0<nBT<47 . 0 0 . 0<nCbH<2 . 0; R3: 0 . 0<RBN<0 . 0 11 . 0<nCs<16 . 0; R4: 238 . 46<MW<270 . 41 57 . 1<H%<63 . 8 402 . 5<SAtot<440 . 301 0 . 0<nR07<0 . 0 -0 . 931<Hy<-0 . 763 0 . 0<ARR<0 . 316 0 . 0<RBN<12 . 0 0 . 0<nCt<3 . 0 ., Musky molecules are heavy and hydrophobic compounds ., This is reflected by a rather large logP , surface area or molecular weight ., From a general point of view , these descriptors reflect well the features of musky odorants ., For the “sandalwood” quality , two rules were observed: R1: 3 . 0<nCrt<5 . 0 1 . 0<nHDon<1 . 0 0 . 0<nR04<0 . 0 1 . 0<nCrq<2 . 0; R2: 3 . 0<nCrt<5 . 0 1 . 0<nHDon<1 . 0 -0 . 429<Hy<-0 . 325 2 . 0<nR05<3 . 0 ., Sandalwood odorants are quite diverse and minor modifications within their structure can abolish the sandalwood note ., The rules which are mined here correspond to models which are very simple and hardly capture the subtlety of this odorant family 28 ., The description presented here corresponds to the prototypic beta-santalol structure which has a campholenic skeleton ., The “almond” quality was described by four rules: R1: 0 . 0<nCp<0 . 0 152 . 443<SAtot<165 . 41 1 . 0<nO<2 . 0 2 . 0<Ui<2 . 585; R2: 0 . 706<ARR<0 . 8 0 . 0<nArCO<0 . 0 1 . 0<nO<1 . 0 3 . 0<Uc<3 . 807 0 . 143<MLOGP<3 . 571 -0 . 917<Hy<-0 . 71 0 . 0<nCb-<2 . 0; R3: 1 . 0<nH<5 . 0 0 . 0<nOxiranes<0 . 0 1 . 0<nHAcc<3 . 0 1 . 0<nN<2 . 0 23 . 79<TPSA ( Tot ) <90 . 27 0 . 0<O%<14 . 3 0 . 0<ARR<0 . 75; R4: 1 . 0<nArCHO<1 . 0 11 . 0<nBT<20 . 0 45 . 0<C%<47 . 1 -0 . 864<Hy<-0 . 668 1 . 0<nHAcc<2 . 0 ., These descriptions suggest that odorants evoking an almond-like quality are compounds bearing at least one oxygen and/or other hydrogen bond-accepting atom but also bearing an aromatic cycle ., This means that the structure bears several unsaturations ., These chemicals are thus relatively small and can be compared to the prototypical structure of benzaldehyde ., Four physicochemical rules described the “orange-blossom” quality: R1: 10 . 0<nCsp2<10 . 0 9 . 23<TPSA ( Tot ) <58 . 89; R2: 1 . 0<nArNH2<1 . 0 213 . 361<SAtot<326 . 286 0 . 0<nR = Cs<0 . 0 0 . 0<nCt<0 . 0 37 . 9<C%<51 . 5; R3: 0 . 773<ARR<0 . 857 39 . 4<H%<45 . 5 9 . 23<TPSA ( Tot ) <52 . 32 3 . 0<nCb-<5 . 0; R4: 47 . 243<Se<53 . 454 4 . 0<nCbH<9 . 0 3 . 287<MLOGP<5 . 007 3 . 0<nHAcc<4 . 0 0 . 231<ARR<0 . 462 ., These descriptions characterize very diverse structures ranging from very small to medium or large compounds ., As a general rule , one can note the presence of unsaturations , consistent with a terpenic structure , associated with a quite hydrophobic feature ., The “jasmine” quality was described by six rules: R1: 12 . 0<nC<13 . 0 43 . 37<TPSA ( Tot ) <44 . 76; R2: 336 . 137<SAtot<337 . 327 0 . 0<nR = Cs<0 . 0; R3: 7 . 0<nCsp2<8 . 0 1 . 0<nCb-<1 . 0 2 . 0<nCp<3 . 0 50 . 0<H%<53 . 3 4 . 0<nCsp3<5 . 0 1 . 0<nCs<3 . 0 1 . 0<nRCOOR<1 . 0; R4: 1 . 0<nCb-<1 . 0 2 . 034<MLOGP<2 . 386 2 . 0<nHet<3 . 0 7 . 0<nCsp2<8 . 0 1 . 0<nCp<2 . 0 -0 . 807<Hy<-0 . 727 0 . 0<nArCOOR<0 . 0 0 . 0<nArOR<0 . 0; R5: 5 . 0<RBN<6 . 0 1 . 0<nRCO<1 . 0 291 . 434<SAtot<350 . 346 10 . 0<nC<13 . 0 0 . 0<nArCO<0 . 0; R6: 1 . 0<nR = Ct<1 . 0 4 . 0<nCs<8 . 0 2 . 0<nCconj<4 . 0 0 . 0<nCt<0 . 0 -0 . 912<Hy<-0 . 873 ., This rule characterizes, ( i )
An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants ., In other words , why do some odorants smell like fruits and others like flowers ?, While the so-called stimulus-percept issue was resolved in the field of color vision some time ago , the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day ., Although a series of investigations have demonstrated that this relationship exists , the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication ., One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality , whereby two very different molecules can evoke a similar odor ., Moreover , the available datasets are often large and heterogeneous , thus rendering the generation of multiple rules without any use of a computational approach overly complex ., We considered these two issues in the present paper ., First , we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities ., Second , we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties ., Third , we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible ., Taken together , our findings provide significant new insights into the relationship between stimulus and percept in olfaction ., In addition , by automatically extracting new knowledge linking chemistry of odorants and psychology of smells , our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling .
An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts ., This is known as the stimulus-percept problem ., Here , we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction ., We also assumed that not only one , but several physicochemical models may describe a given olfactory quality ., To achieve this aim , a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities ( e . g . fruity , woody ) ., A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features ., By meeting these 2 challenges , we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody ( among others ) ., For most qualities , multiple ( rather than a single ) chemical models were generated ., These findings provide new elements of knowledge about the relationship between odorant chemistry and perception ., They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products .
smell, chemical compounds, statistics, social sciences, neuroscience, data mining, perception, physicochemical properties, cognitive psychology, scientists, forecasting, odorants, mathematics, materials science, information technology, science and technology workforce, physical chemistry, chemical properties, research and analysis methods, physical properties, computer and information sciences, mathematical and statistical techniques, chemistry, physics, people and places, professions, psychology, science policy, careers in research, biology and life sciences, population groupings, materials, physical sciences, sensory perception, cognitive science, phenols, statistical methods