ML for Scent
Alex Wiltschko, Benjamin Sanchez-Lengeling, BrianLee, Carey Radebaugh, Emily Reif, Jennifer Wei

Hi!

I’m Alex Wiltschko, a scientist at Google Research.
I lead a research group within Google Brainthat focuses on machine learning for olfaction.

Google Research

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18 oﬃces, 11 countries

Make machines intelligent. Improve people’s lives.

Our Approach● Foundational research● Building tools to enable research & democratize AI/ML● AI-enabling Google products

What’s our goal?
Do for olfaction what machine learning has already done for vision andhearing.
To digitize the sense of smell, and make the world’s smells andﬂavors searchable. Every ﬂower patch, every natural gas leak, every
item on every menu in every restaurant.

We’re starting at the very beginning, with the simplest problem…
but ﬁrst, some olfaction facts!

Most airflow is not smelled. Passes righton through the lower turbinates to yourlungs.
The OSNs are one of two parts of yourbrain that are exposed to the world (theother is the pituitary gland, and that’s inblood, so only half-counts).
Taste lives on your tongue. Flavor isboth taste and retronasal olfaction, froma “chimney effect”.

GPCR: G-protein coupled receptor
OR: GPCR Olfactory Receptor
OSN: Olfactory sensory neuron~400 ORs expressed in humans (asopposed to 3 types of cones)~1000 in mice. ~2000 in elephants!
One OR per OSN.
ORs comprise 2% of your genome,
but many are pseudogenes.
OR structure is unknown, they areuncrystallized. Further, only ~40
expressed in cell lines.
Their ligand responses are broadlytuned, but many ORs (22/400) are stillorphans, with no known ligand.

People do smell different things!
SNPs in single ORs result in sensory dimorphisms. The most famous
ones are:
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OR7D4 T113M: normally funky beta-androstenone (boar taint)
is rendered pleasant.
OR5A1 N183D: nearly completely Mendelian. Carriers of themutation can detect beta-ionine at two orders of magnitude
lower concentration
Olfactory sensory dimorphisms are likely common — humans
differ functionally at 30% of OR alleles.
~4.5% of the world is colorblind (CBA)
13% in the US has selective hearing loss (NIDCD)
All this to argue — smell is not defacto finicky or illogical.

Right now, we’re starting with the simplest problem

Mainland et al 2015

“Smells sweet, with a hint ofvanilla, some notes of creamy andback note of chocolate.”

Predict

Odor
descriptors

And why is this hard?

We built a benchmark from perfumery raw materials

We built a benchmark from perfumery raw materials
Vanillin
1: sweet, vanilla, creamy, chocolate
2: sweet, vanilla, creamy, phenolic
General agreement between repeated
ratings. All ratings by perfume experts.

We built a benchmark from perfumery raw materials...
solventorangeﬂower
breadyblack currant
radishwoody

ﬂoral

green
sweetfruityWe built a benchmark from perfumery raw materials
odorsodors

Ohloﬀ’s ruleBajgrowicz and Broger’s ambergris
osmophore model
Buchbauer’s santalols
Boelens’ synthetic muguet

Historical SOR approaches
Pen & Paper
Kraft’s vetiver rule(-)-khusimone

4,7,7-Trimethyl-1-methylidene
spiro[4.5]decan-2-one

1,7-cyclogermacra-1(10),4-dien-15-alFig 3.22 Scent and Chemistry (Ohloﬀ, Pickenhagen, Kraft)

Rule-based principles for predicting odor. There are as many exceptions as there are rules.

Traditional Computational Approaches

Predict●●●●●“bag of sub-graphs” representation
AKA molecular ﬁngerprints

Toxicity
Solubility
Photovoltaic
eﬃciency (solar cell)
Chemical potential(batteries)...

“cat”“dog”“car”“apple”“ﬂower”
Labeled Photos

Unlabeled PhotoInputPIXELS

AUDIO“Hello,how areyou?”TEXTPIXELS

Output“lion”“How cold is itoutside?”“你好，你好吗？”“A blue and yellow train
travelling down the tracks”

Graphs as input to neural networks: not just images,
sounds or words

Inside a GNN
Converting a molecule to a graphInside a GNN
Propagating information & transforming a graphA GNN to predict odor descriptors

And how well can we predict?

A representation optimized for odor

Last layer embeddings
63 dimension vector

Exploring the geometric space of odor

Exploring the geometric space of odor

What do nearby molecules look like?
Inspired by word embeddings. Are there “molecular synonyms”?

First, what do “nearest neighbors” look like if you use
just structure, and ignore our neural network?
Then, what do nearest neighbors look like to our GCN?

Molecular neighbors: using structure
berry,
medicinal,
fruity,phenolic

medicinal,
sweet, fruity,
ﬂoral

ortho-cresyl isobutyrate

ortho-cresyl acetate

dihydrocoumarinspicy

herbal, nutty, coconut, coumarinic,
cinnamon, sweet, hay, tobaccosmoky,spicy,
balsamic

ethyl 3-(2-hydroxyphenyl)
propionate

sweet,
phenolic,
ﬂoral
Tolyl decanoate

Acetyl thymol

Molecular neighbors: using GCN features
green,
coumarinic
phthalide1,4-benzodioxin-2(3H)-one

phenolic, hay,lactonic,
coconut,
coumarinic,
almond, sweet,
powdery

dihydrocoumarin
green,
vanilla,nutty,
coumarinic,
spicy

herbal, nutty, coconut, coumarinic,
cinnamon, sweet, hay, tobacco

sweet,
nutty,
almondcoumanesweet,
coumarinic,
hay
coumarin

2-benzofurancarboxaldehydeDo these representations generalize?
Using a learned model to make predictions on a new task is ‘transfer learning’

You might hear ‘ﬁne-tuning’ referred to as a strategy for‘transfer learning’.
Transfer learning in chemistry, today, rarely works. Do ourembeddings transfer learn to other tasks?

Do these representations generalize?

DREAM Olfactory Challenge

Dravnieks

Transfer-learned to achieve state-of-the-art on the two major olfactory benchmark tasks

But why is the neural network making these predictions?
Toy test example: classify whether a molecule has benzene. Which atoms contribute to predictions?

Benzene?

This is just one task of potentially hundreds, of varying complexity.

But why is the neural network making these predictions?
Toy test example: classify whether a molecule has benzene. Which atoms contribute to predictions?

But why is the neural network making these predictions?
Toy test example: classify whether a molecule has benzene. Which atoms contribute to predictions?

Positive examples

Negative examples

But why is the neural network making these predictions?
Odor percept — “garlic”

Positive examples

Negative examples

But why is the neural network making these predictions?
Odor percept — “fatty”

Positive examples

Negative examples

But why is the neural network making these predictions?
Odor percept — “vanilla”

Positive examples

Negative examples

But why is the neural network making these predictions?
Odor percept — “winey”

Positive examples

Negative examplesΣ( )Future Directions
Collecting interest & those interested in collaborating.
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Test ML-driven molecular design forhumans in a safe context.

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Build bedrock understanding insingle-molecules before working on
odor mixtures●

Build a foundational dataset for the MLon molecules community.

Benjamin Sanchez-Lengeling
Brian Lee
Carey Radebaugh
Emily Reif
Jennifer Wei
Alex Wiltschko