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The third component, it seems to me (beyond greater synthetic tractability – the subject du jour – and better property prediction) is computer-assisted generation of novel candidate structures.
I think that every synthetic organic chemist should take a look at in Angewandte Chemie. It’s on the application of computer algorithms to planning synthetic routes, which is a subject that’s been worked on for fifty years or more – without, it has to be said, making too much of an impression on working organic chemists. But after reading this paper, from Bartosz Grzybowski and co-workers at the Polish Academy of Sciences, I’m convinced that that is going to change. I don’t know if this particular software, or even this particular computational approach (which I last is going to do it, although they both look very promising. But to a first approximation, those conditions don’t matter – what seems inescapable to me is that the generation of synthetic routes in organic chemistry is subject to automation, and we are getting very close to seeing this put into use.
The latest OrgChemFront issue is published online now
When you listen to his talks Burke IS persuasive, but then reflecting on the real impact on small molecule SAR chemistry … doubt is getting the best of this concept. Peptides and nucleotides are made via one single connection from a rather limited number of building blocs. No way this approach will prove useful for the diversity of organic building blocks and bond types available to the organic chemist. Being mean: this is the defrosted version of “high throughput paralell synthesis” – anybody seen something useful coming out of that hype ever ?
It’s nice work, no doubt – congrats to Burke, will chip the best out of nanomaterials and other dendirmers, but get your brains working again. There are receptors waiting for your molecules …
In 2011 Zen19D reached 5 dan in Go (scale of 1-9 for professionals). 5 years later, AlphaGo is the best Go player that has ever existed. Although I would estimate that synthesis is a tougher problem than Go (mostly because of imperfect/incomplete information in the literature and complicated stereoelectronic and functional group compatibility issues), Go was a MUCH tougher problem than chess, and chess was a MUCH tougher problem then checkers, and checkers was a MUCH tougher problem than [so on and so forth]. Many AI experts thought we were decades from a computer that could defeat a 9 dan professional without handicaps. Point is, AI almost always progresses faster than you think. As it gets better at what it does, it gets better at getting better at what it does. Programs for organic synthesis are no different. If a program can be the approximate equivalent of an undergraduate today, it will be a graduate student in 5 years, and a professor 2 years after that, then one month later it’s hanging Baran’s Ingenol synthesis up on it’s refrigerator, patting him on the head and telling him how cute it was that he came up with that all on his own. But most practitioners will bury their head in the sand here and insist that they could never be replaced by computers.
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But these now seem like solvable problems, and that’s what I want to emphasize. There seems to be no reason why time, money, and effort cannot continue to make this sort of approach work better and better – and it’s already working better than most people realize. In fact, I’m willing to stipulate that software will, in fact, eventually (how soon?) turn out to provide plausible synthetic routes to most compounds that you give it, with the definition of “most” becoming progressively more stringent as time goes on. So where exactly does that leave us organic chemists?
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