Digitizing Smell: Using Molecular Maps to Understand Odor

In case someone is interested about this domain. There was an interesting challenge around it on AIcrowd last year.

It was called 'Learning to Smell' and hosted by popular swiss fragrance and flavour business Firmenich. It contains publicly available dataset, baselines, winner codebases, etc., to play around with.

[1] https://www.aicrowd.com/challenges/learning-to-smell

[2] https://discourse.aicrowd.com/c/learning-to-smell/357

[3] https://www.aicrowd.com/challenges/learning-to-smell/noteboo...

Disclaimer: I have been affiliated with AIcrowd.

Some fluff in this PR piece, but I love the message that molecular chemistry can now also benefit from deep learning advances, thanks to graph neural networks. Because molecules are by their nature graphs of atoms, it makes sense to learn a latent representation of a molecular entity with a GNN, and use that for various classification or prediction tasks.

Makes me wonder if the computationally expensive geometry optimization calculations using DFT etc. can be partially done by a GNN. So, pre-optimize a structure with a computationally cheaper GNN to speed up convergence.

> recently made discoverable by Google Books, which we subsequently made machine-readable

You might say "WTF? Google Books already made it machine-readable. "

The problem, as you'll discover here;

https://www.theatlantic.com/technology/archive/2017/04/the-t...

is legal and political. Google has it in digital form, but they can't give it to you, sell it to you, show it to you, or do anything else with it. So the researchers had to re-digitize it. Or maybe ("The Google Books team brought the USDA dataset online.") the researchers had some special help from Google.

exciting direction in this largely unexplored area. Empirically we have done a bunch of work with correlating mixtures of molecules to human perception at Volatile AI (https://volatile.ai) and the degrees of variation in mixtures are really wild - a tiny amount of something in a mixture can change the smell perception of the whole mixture. So getting decent results will be so much harder when looking beyond single molecules

Thinking about smell a few years ago I realized that individual differences in odor perception might be due to missing smell receptors.

In color vision we have only three different kinds of cones. Color blindness due to a missing type of cone is present in a substantial portion of the population.

I believe one of my children is colorblind, and it took a long time to figure that out. With a missing smell receptor it would be even more difficult to detect the differences between people.

If you want to have an idea of the molecular mechanisms involved, last year the structures of an insect odorant receptor in complex with two different odorants was published: https://www.nature.com/articles/s41586-021-03794-8

This is very cool.

It would be great if they could also create an olfactory-nerves-electrical-activity to odor map.

Then maybe with that, a device could be built that you could put in your nose and feel different smells. Kind of like a VR headset but for your nose.

Obligatory: https://archive.google.com/nose/

Does this mean we will finally get Smell-o-Vision?

I’ve been wondering for years if it might be possible to use a UAV with a pretty hefty sniffer to check out some areas and detect cannabis pollen. Then it would maneuver toward the origin based on wind data and if it hits it would photo and log GPS and auto fly to base ASAP. Then Subcontract to some hardcore criminals to go rip it off and flip it. Totally risk free money!