|
I used to work at a drug discovery startup. A simple model generating directly from latent space 'discovered' some novel interactions that none of our medicinal chemists noticed e.g. it started biasing for a distribution of molecules that was totally unexpected for us. Our chemists were split: some argued it was an artifact, others dug deep and provided some reasoning as to why the generations were sound. Keep in mind, that was a non-reasoning, very early stage model with simple feedback mechanisms for structure and molecular properties. In the wet lab, the model turned out to be right. That was five years ago. My point is, the same moment that arrived for our chemists will be arriving soon for theoreticians. |
For instance, you can put a thousand temperature sensors in a room, which give you 1000 temperature readouts. But all these temperature sensors are correlated, and if you project them down to latent space (using PCA or PLS if linear, projection to manifolds if nonlinear) you’ll create maybe 4 new latent variables (which are usually linear combinations of all other variables) that describe all the sensor readings (it’s a kind of compression). All you have to do then is control those 4 variables, not 1000.
In the chemical space, there are thousands of possible combinations of process conditions and mixtures that produce certain characteristics, but when you project them down to latent variables, there are usually less than 10 variables that give you the properties you want. So if you want to create a new chemical, all you have to do is target those few variables. You want a new product with particular characteristics? Figure out how to get < 10 variables (not 1000s) to their targets, and you have a new product.