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Fitted Pleasantness

Ple

asan

tnes

s

(R= 0.493, P<0.001)

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6

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Complexity

Ple

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s

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Complexity

Kermen Model Prediction

Previous Models

Challenge Overview

[1]

Odor Intensity

Boelens model

Predicting olfactory perception from chemical structureChung Wen Yu1, Yusuke Ihara1,2, Joel D. Mainland1,3

1Monell Chemical Senses Center, Philadelphia, PA2Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Japan

3University of Pennsylvania, Philadelphia, PA

• Goal: Predict olfactory perception using chemical structure• Data: 49 human subjects rated the perceptual features of 476 odorants • Subchallenge 1: Predict the ratings of every subject• Subchallenge 2: Predict the mean and standard deviation of ratings across subjects

• Khan et al., 2007 predicted the pleasantness of odorants using a linear model with the �rst seven principal components of physicochemical space [1].• This model had similar performance on the DREAM challenge dataset.

• Kermen’s model [2] found that molecular complexity (a combination of size and symmetry) predicted odor pleasantness.• This model does not perform as well as Khan’s model.

Khan et al., 2007

Kermen et al., 2011

Khan et al. (2007) Figure 5. C

Khan Model Prediction

(R= 0.304, P<0.03)

Complexity Model (Kermen et al., 2011)

Ple

asan

tnes

s

(R= 0.286, P<0.001)

• The intensity model relies on structural features to make predictions. • Boelens model predicts whether or not molecules have an odor based on their volatility and lipophilicity [4].• Previous studies predicting olfactory thresholds modeled air to receptor transport [5,6].

−4

0

4

−200 0 200 400

Boiling Point (°C)

logP

ethylene glycolwaterglycerin

sorbitol

maltitol

ca�eine

L-Arginine

L-Histidine

TNT

ethyl salicylate

ethane

propanebutane

methane

Krypton

acetone

ethyl mercaptan

ethanol

carbon monoxide

hexanepentane

O

O

O

OH

OH

OH

OH

OHHO

HO

OH

OH

HO

HO

HO

OH

OH

OHH

O

O OH

OHHO

HO

OH

OH

OH

OH

HOOH

OHHO

HO

OH

OO

H3 C

N O

N

O

CH 3

CH 3

N

N

H 3 C

CH 3

N+

O

O–

N+

OO–

N+

O

O–

CH 3

O

H3 C

H3C SH

Alkanes

0.0

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INTE

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ITY

SW

EE

TVA

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ITC

HE

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GA

RLI

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SP

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Generate Predictive models

• Features: we used physicochemical descriptors [3], Morgan �ngerprints, and NSPDK �ngerprints. • Initial data cleaning: we removed non-informative variables, and performed cube-root transformation and normalization. • Model building: we built predictive models using the Extra-Trees algorithm • Cross-validation: we performed 5-fold CV, repeated twice. −0.2

0.0

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0.6

BUR

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S

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CH

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Sim

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Similarity Features

PhysicochemicalFeatures

X1v

X0sol

VvdwMG

Vx

MW

X1sol

ATS1p

X0v

AMR

Dilution

0.0 0.1 0.2

Variable Importance Score

0.25

0.50

0.75

0 50 100

150

200

6823

Number of Variables

r val

ue CVLBTest-Retest

0.76

Moskone Musk ketone

Musk xylene Musk ambrette

Musks A B C

D E F

G H I

J K L

M N O

WiA_B.m.

NSPDK155708

SpMaxA_B.m.

Ho_Dz.Z.

Ho_Dz.m.

NSPDK8767

NSPDK8768

NSPDK56642963

SpMax7_Bh.s.

NSPDK61229

−0.1 0.0 0.1

Variable Importance Score

Odor Pleasantness

NSPDK61229:Acetylvanillin

NSPDK155708:Ethyl vanillin acetate

2D Structual Matrix:atomic mass, polarizability,

charges, etc.+

0.25

0.50

0.75

0 50 100

150

200

6823

Number of Variables

r val

ue CVLBTest-Retest

0.71

• 20 molecules were rated twice by each subject, allowing us to calculate the test-retest correlation. • Test-retest sets the ceiling for predictive models.• Averaged subject ratings are more reliable than individual subject ratings.

0.00

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0.50

0.75

FRU

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EG

AR

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Test-RetestSubchallenge 1Subchallenge 2Best DREAM model Subchallenge 2

Test-Retest

1. Khan M.K., Luk C., Flinker A., Aggarwal A., Lapid H., Haddad R., Sobel N. (2007). Predicting Odor Pleasantness from Odorant Structure: Pleasantness as a Re�ection of the Physical World. The Journal of Neuroscience. 27(37):10015-10023. 2. Kermen F., Chakirian A., Sezille C., Joussain P., Le Go� G., Ziessel A., Chastrette M., Mandairon N., Didier A., Rouby C. & Bensa� M. (2011). Molecular com-plexity determines the number of olfactory notes and the pleasantness of smells. Scienti�c Report. DOI: 10.1038/srep002063. Talete srl, DRAGON (Software for Molecular Descriptor Calculation) Version 6.0 - 2012 - http://www.talete.mi.it/4. H. Boelens. (1983). Structure-activity relationships in chemoreception by human olfaction. TIPS. 421-4265. Abraham MH., Sanchez-Moreno R., Cometto-Muniz JE., Cain WS. (2012). An Algorithm for 353 Odor Detection Thresholds in Humans. Chem. Senses 37: 207-2186.Hau KM., and Connel DW. (1998). Quantitative Structure-Activity Relationships (QSARs) for Odor Thresholds of Volatile Organic Compounds (VOCs). Indoor Air 8: 23–33

Bakery - Vanillin

NSPDK61660:S-Furfuryl thioacetate

(sulfurous)

NSPDK62131:Furfuryl Methyl Disul�de

(alliaceous)

NSPDK7363:Furfuryl Mercaptan

(co�ee)

Burnt - Furfuryl

NSPDK539829:Vanillin Isobutyrate

Morgan8467:Ethylvanillin

NSPDK1183:Vanillin

Decayed - Thiol

0.15

morgan6997:2-Ethylphenol

morgan18515:2-Butylpheno

morgan6943:2-Isopropylphenol

NSPDK62444:S-Methyl thiobutyrate

(Cheesy)

NSPDK13582:4-Butyrothiolactone

(Garlic)

NSPDK7969:Benzenethiol

(Meaty)

Chemical - Phenol

Conclusion• Averaged subject ratings are reliable for both odor intensity and pleasantness. Most of the perceptual descriptors show great within and across subject variance. • The intensity model performed comparably to the test-retest correlation, while the pleasantness model performed worse than the test-retest correlation. • Models for the 19 descriptors performed worse than intensity and pleasantness models. • Some models rely on physicochemical features, whereas others use molecular template-matching.

Literature Cited

Predicting Odor from Structure