The race to build machines that can synthesize any organic compound is heating up. Below you can find some very interesting snippets from a nature article on "robo-chemists" but you are better off reading the article in full. Note that the synthesis machines discussed are way more complex than ones currently in use or the more advanced chemprinters in development. The machines themselves would certainly be marvels of engineering but the hardest part will lie in the development of their brains, the software that would understand chemistry well enough to predict what'll work and what won't.
Organic chemists typically plan their work on paper, sketching hexagons and carbon chains on page after page as they think through the sequence of reactions they will need to make a given molecule. Then they try to follow that sequence by hand — painstakingly mixing, filtering and distilling, stitching together molecules as if they were embroidering quilts.
But a growing band of chemists is now trying to free the field from its artisanal roots by creating a device with the ability to fabricate any organic molecule automatically. “I would consider it entirely feasible to build a synthesis machine which could make any one of a billion defined small molecules on demand,” declares Richard Whitby, a chemist at the University of Southampton, UK.
A British project called Dial-a-Molecule is laying the groundwork. Led by Whitby, the £700,000 (US$1.2-million) project began in 2010 and currently runs until May 2015. So far, it has mostly focused on working out what components the machine would need, and building a collaboration of more than 450 researchers and 60 companies to help work on the idea.
Some reckon it would take decades to develop an automated chemist as adept as a human — but a less capable, although still useful, device could be a lot closer. “With adequate funding, five years and we're done,” says Bartosz Grzybowski, a chemist at Northwestern University in Evanston, Illinois, who has ambitious plans for a synthesis machine of his own.
Grzybowski has spent the past decade building a system called Chematica and designed it to take a holistic view of synthesis: it not only hunts for the best reaction to use at each step, but also considers the efficiency of every possible synthetic route as a whole. This means that a poor yield in one step can be counterbalanced by a succession of high-yielding reactions elsewhere in the sequence. “In 5 seconds we can screen 2 billion possible synthetic routes,” says Grzybowski.
When Grzybowski first unveiled the network behind Chematica in 2005 (ref. 3), “people said it was bullshit”, he laughs. But that changed in 2012, when he and his team published a trio of landmark papers showing Chematica in action. For example, the program discovered a slew of 'one pot' syntheses in which reagents could be thrown into a vessel one after the other, without all the troublesome separation and purification of products after each step. Chematica can also look up information about the cost of starting materials and estimate the labour involved in each reaction, allowing it to predict the cheapest route to a particular molecule. When Grzybowski's lab tested 51 cut-price syntheses suggested by Chematica5, it collectively trimmed costs by more than 45%.
As long as programmes like Chematica rely on databases of published studies, says Whitby, they will struggle to design reliable synthetic routes to unknown compounds. To build a synthesis machine, “we need to be able to predict when a reaction is going to work — but more importantly we need to be able to predict when it's going to fail”.
Unfortunately, those failures are rarely recorded in the literature. “We only publish the successes, a cleaned-up version of what happens in the lab,” says Whitby. “We also lose a lot of information: what really was the temperature, what was the stirring speed, how much solvent did you use?” One solution is to record those successes and failures using electronic laboratory notebooks (ELNs), computer systems for logging raw experimental data that are widely used in industry but still rare in academia. “A lot of people ask, 'Who reads all these data?' The point is that machines use them — they can search the data,” explains Mat Todd, a chemist at the University of Sydney in Australia.
“If we really did know the history of every chemical reaction that had ever been done, we'd have amazing predictive capabilities,” says Todd. Many of those dreaming of a synthesis machine agree that widespread data harvesting will require a huge cultural shift. “That's absolutely the biggest barrier,”. “In chemistry, we don't have that culture of sharing, and I think it's got to change.”