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In Silico Olfaction



Introducing Ohloff, our in-silico Olfactory Model


As many of you have already noticed, I have been bragging about my computer model of the human olfactory system. We decided to name it Ohloff, in an hommage to prominent German fragrance chemist Günther Ohloff. Olfactory is one of the least understood sensory modalities of the human body and other animals. Until recently, it was also the least advanced one in terms of our computer models.

I am an avid supporter of openness in the fragrance industry, yet as this is a part of a commercial project, I can't expose all the details. Nonetheless, feel free to ask whatever you want, and I will do my best to answer within my limitation.

Not going into too many details, our model is based on a curated database of aroma molecules. This website actually uses the same database to provide our usage statistics. The database contains many more molecules (about 5000), for which we do not have enough usage data but are still very useful for modeling the sense of smell. This one-of-a-kind database, combined with our breakthrough Machine Learning pipeline, enables this never before seen type of model.

I promise to tell you more about it soon, but until then, I feel like showing off some photos of the patterns we receive for a few of the more famous odor molecules. Finding their similarities will be left as an exercise (I will share my insights in a future post).

Musk Ketone.png
Ethylene Brassylate.png
musks_activation (1).png
musks_activation_focused (1).png

To be continued...

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