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As an alternative perspective: I worked in a lab that was relying heavily on intuition and heuristics for exploratory synthesis. By digitizing all of the lab's

• makmanalp 10y ago
  In other words, having that good intuition heuristic matters less when you can brute-search a wider range of options. Of course, no one likes being told that idiotically testing many many options is better than "intelligence". I suspect we programmers are going to be OK with this idea, until we're replaced too. :-)
  • jsprogrammer 10y ago
    Brute-search is not idiotic testing. It is many times much faster to automate the search and then to move on to the next level/parameter (where you may get to start exploring again).
    • dnautics 10y ago
      it might not be that easy. What happens when each iteration takes 30 minutes of setup, 4 hours -> overnight reaction time, 20 minutes of concentration, and a 30 minute silica gel column?
      • jsprogrammer 10y ago
        You parallelize and pipeline.
      • pbhjpbhj 10y ago
        Interns?
    • HarryHirsch 10y ago
      Brute-force search is idiotic testing, almost by definition. The smart approach is sparse matrices, optimized either by staring at mountains of data (possible computer-assisted), or by gut feeling. Oftentimes the search space is so large (protein crystallization involves at least buffer, precipitant, pH, concentration) that you need to look at promising spots in the search space.
    • dekhn 10y ago
      brute force is "naive" testing. it's almost never better (I say this as the person who ran exacycle, the world's largest brute force supercomputer).
  • dnautics 10y ago
    There will still be cases where this isn't possible. In the chemistry case, let's say you look at the structure of your starting material and your product and notice that it has a functional group that your catalyst can be tethered to with a particular reach length and chirality to reach the reaction site... Unless the computer "understands" these concepts building on some other set of principles, it won't be able to creatively generate solutions to those sorts of problems. In some cases, the "n" of directly analogous situations may be in the low single digits or even zero.
• dnautics 10y ago
  from original article: "One of the most striking features of chemistry as a science is that very palpable properties like color, smell, taste and elemental state are directly connected to molecular structure." 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.
  • thaumasiotes 10y ago
    >> One of the most striking features of chemistry as a science is that very palpable properties like color, smell, taste and elemental state are directly connected to molecular structure. 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.
    • dnautics 10y ago
      Yeah that's in the Op's linked article, but would be unaddressed by the sub thread linked article's technique.
  • dharma1 10y ago
    https://arxiv.org/abs/1507.02672 https://arxiv.org/abs/1507.02672 Ladder networks get 1.62% error rate on MNIST with 50 labeled samples