We use a lot of automated equipment in the drug discovery business. There’s an awful lot of grunt work involved, and in many cases a robot arm is better suited to the task – transferring solutions, especially repetitive transfers of large numbers of samples, is the classic example. High-throughput screening would just not be possible if you had to do it all by hand; my fingers hurt just imagining all the pipetting that would involve.
But I wouldn’t say that the process of medicinal chemistry is at all automated. That’s very much human-driven, and a lot of the compounds on most med-chem projects are made by hand, one at a time. Sure, there are parallel synthesis techniques, plates and resins and multichannel liquid handlers that will let you set up a whole array of reactions at once. But you do that, typically, only after you’ve found a hot compound, and that’s often done the old-fashioned way. (And, of course, there are a lot of reactions that just don’t lend themselves to efficient parallel synthesis).
But I remember the first time I saw an automated synthetic apparatus, back at an ACS meeting in the mid-1980s. There was a video in the presentation (a real rarity back then), and it showed this Zymark arm being run to set up an array of reactions, assay each of them after an overnight run, and report on the one that performed the best. “Holy cow”, I thought, “someone’s invented the mechanical grad student”. Being a grad student at the time, I wasn’t so sure what I thought about that.
This all comes to mind after reading a report over at Wired about a robotic system that has been claimed to have made a discovery without much human input at all. “Adam”, built at Aberystwyth University in Wales, seems to have been set up to look for similarities in yeast genes whose function hadn’t yet been assigned, and then (using a database of possible techniques) set up experiments to test the hypotheses thus generated. The system was also equipped to be able to follow up on its results, and eventually uncovered a new three-gene pathway, which findings were confirmed by hand.
And Ross King, leading the project at Aberystwyth, is apparently extending the idea to drug discovery. Using a system that (inevitably) will be called “Eve”, he plans to:
. . .autonomously design and screen drugs against malaria and schistosomiasis.
“Most drug discovery is already automated,” says King, “but there’s no intelligence — just brute force.” King says Eve will use artificial intelligence to select which compounds to run, rather than just following a list.
Well, I won’t take the intelligence comment personally; I know what the guy is trying to say. I’ll be very interested to see how this is going to be implemented, and how it will work out. (I’ll get an e-mail off to Prof. King asking for some details). My first thought was that Eve will be slightly ahead of a couple of the less competent people I’ve seen over the course of my career. And if I can say that with a straight face (and now that I think about it, I believe that I can), then there may well be a place for this sort of thing. I’ve long held that jobs which can be done by machines really should be done by machines.
But how is this going to work? The first way I can see running a computational algorithm to design drugs would be some sort of QSAR, and we were just talking about that here the other day – most unfavorably. I can imagine, though, coding in a lot of received wisdom of drug discovery into an expert system – Topliss tree for aryl substituents, switch thiophene for phenyl, move nitrogens around the rings, add a para-fluoro, check both enantiomers, put in a morpholine for solubility, mess with the basicity of your amine nitrogens, no napthyls if you can help it, watch your logD – my med-chem readers will know just the sorts of things I mean.
Now, automating that, along with feedback from the primary and secondary assays, solubility, PK, metabolite ID and so on. . .mix it in with literature-searching capability for similar compounds, some sort of reaction feasibility scoring function, ability to order reagents from the stockroom, analyze the LC/MS and NMR traces versus predictions, weight the next round of analogs according to what the major unmet project goals are. . .well, we’re getting to the mechanical medicinal chemist, sure enough. Now, not all of these things are doable right now. In fact. some of them are rather a long way off. But some of them could be done now, and the others, well, they’re certainly not impossible.
I’m not planning on being replace any time soon. But the folks cranking out the parallel libraries, the methyl-ethyl-butyl-futile stuff, they might need to look over their shoulders a bit sooner. That’s outsourcing if you like – from the US to China and India, and from there to the robots. . .