What is chemical intuition?
wavefunction.fieldofscience.com
wavefunction.fieldofscience.com
I think the similarity lies in that both are "black box" processes, where you have to "dictionary knowledge" of existing facts, but not in the "lookup" sense, but in the "spin creative analogies". Then what remains is to formalize the imagined analogy and check to see if the result is confirmed by reality. In the case of proofs, it's writing it down and checking the logic of each step and making sure the axioms lead to the conclusion. In the case of (synthetic) chemistry, it's doing the labor of the reaction and seeing if you get the desired product.
In my case, I looked at the structure of an enzyme and guessed that it was functioning as a PNP transistor, and for my purposes alleviating the high-energy region in the center was probably a good idea - turning it into (effectively) a direct conductor instead of a 'semiconductor'. Making that alteration (justified by the literature) resulted in a 4x improvement in enzyme yield. I don't know for sure if the analogy is exactly correct (didn't have the tools to suss that out), but that's what intuitively "inspired" the strategy that I pursued.
http://www.nature.com/news/computer-gleans-chemical-insight-...
So, I guess my question is: to what degree is this reliance on intuition really just a lack of good data driven methods?
If the lab notebook doesn't contain that information, no amount of machine learning will find it. There's also intuition in subtle things that don't necessarily make it into the lab notebook (did I shake the extractor vigorously or just a little bit?). Or when running on autopilot, maybe you didn't do something that you wrote down you did - that makes a difference.
If you could automate lab note taking, that would be excellent.
Moreover, this 'principles based search' in the subthread article is about crystallization, which is notorious for being a tractable solution based on high-throughput methodologies (and thus has massive parallelization as a worked out strategy). Synthesis, by contrast, is a low-n experience. When machine learning techniques get good at classification by solving MNIST by looking at 3-5 samples of each letter, then maybe I'll be more optimistic.
That doesn't sound so striking. Smell and taste are both direct physical assessments of molecular structure. There's no model where they could fail to be related to molecular structure.
Ladder networks get 1.62% error rate on MNIST with 50 labeled samples
One type of unfair advantage which there is no substitute for.
So much so for ths particular field of natural science.