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Author Topic: Has AI Discovered a Drug Now? Guess.  (Read 7017 times)

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Has AI Discovered a Drug Now? Guess.
« on: September 06, 2019, 06:11:19 PM »

Has AI Discovered a Drug Now? Guess.


4 September, 2019

Here is an interesting paper in Nature Biotechnology on computational drug design, and if you read it without reading any of the accompanying articles about it, you will have a perfectly good time. There are things that you will be impressed by, and there are things that you will argue with, but that’s how most papers go, right?

But if you read the articles about the paper, your experience will be very different indeed. So let’s do the authors (a team led out of Insilico Medicine) the favor of first talking about what they actually wrote, then we’ll get to what a lot of people seem to believe.

This is work on “generative” drug design, which as the name implies, is trying to generate new structures rather than evaluate existing ones. The idea (which does not apply only to chemical structures) is that you train a computational system on all sorts of existing knowledge and data, and it then comes up with new stuff that seems as if it would fit the existing framework.

Now, this is what human medicinal chemists do all the time – look over a bunch of compounds that are ligands for a certain protein and say “Hmm, I think we should try a seven-membered ring here to change that dihedral angle” or “What happens if we make that aryl more electron-poor?” and the like. So generative methods are one of the big things that people have had in mind over the years when they think about what they’d really like computational methods to do for them: “Find me some ligandsWiki” And some major subsets of that request are first “Find me some more ligands, because we need some patentable chemical matter”, then second (harder) “Find me some more ligands, because the existing ones all have problems with selectivity/metabolism/stability”, then third (even harder) “Find me some ligands, because no one has ever screened against this protein and we’d like to save time and just go right to active compounds” and fourth (hardest) “Find me some ligands, because people have screened actually this before and always come up empty”.

You can also consider these “virtual screening” efforts by the sorts of chemical matter they’re going to be searching through to come up with those ligands. You could start with a list of actual compounds that you have more or less on hand, such as a list of known drugs, a chemical supplier’s catalog(s), or your own screening collection. Or you could extend into not-made-yet territory, first in relatively easy ways (here’s another hundred amines that could be used to make the amide at this position), and moving on to harder ones that don’t resemble existing chemical matter so much. That’s the “generative” part.

By those classifications, the current paper fits into the “Find me some patentable chemical ligands, which means that you’re going to have to come up with new scaffolds” class, because it’s targeted at discoidinWiki domain receptor 1 (DDR1), a tyrosine kinase target that’s been the subject of quite a bit of drug discovery work already.

As the authors note, at least eight different chemotypes have been reported for it in the last few years, so it’s safe to say that finding DDR1 chemical matter is not in itself an outstanding problem in drug discovery. But it’s a good proving ground for a technique like this, which is really on the outer fringes of what’s currently possible and thus needs all the help it can get.

The authors describe a software approach acronymed GENTRL (generative tensorial reinforcement learning), which involved training the system up on all the existing DDR1 literature, the larger set of kinase inhibitors in general, databases of medicinally active structures, and an even larger set (17,000) of compound structures that have been specifically claimed in all sorts of med-chem patents. This allows the software to do a multilayer optimization to propose structures that are (1) more likely to hit DDR1 itself, (2) more likely not to hit other kinases, and (3) more likely to not resemble structures that are already patented. And those are just the sorts of starting points that you’d be trying to find.

How did it do? The initial output was 30,000 structures, so the problem then became how to narrow this down. It’s worth noting that a high-throughput screen that produced 30,000 hits would be considered to have failed, but that’s because in that case you’re screening against a large assortment of chemotypes, the great majority of which are surely not going to be real hits – in this case, the idea is that the software is supposed to be zooming in on those real hits to start with.

Still, 30,000 starting points is perhaps not all that much of a zoom. They cleared out structures based on molecular weight, number of polar groups, etc., and that knocked it down to about 12,000 compounds. These cutoffs were far more generous than “Rule of 5” cutoffs (cLogP values from -2 to 7 were deemed OK, for example), so the original set must have had some pretty shaggy structures in it. The next round cleared out structures that seemed unstable or reactive, which took it down to about 7900 compounds.

They then applied clustering and chemical diversity sorting to the remaining structures, and cleared out the too-similar ones to leave about 5500 molecules, and these were reduced to 4600 by scoring for too-close similarity to commercially available compounds. This set was then scored again through the program’s kinase-evaluating filters and also fit to pharmacophore models derived from known DDR1 ligand-bound X-ray structures. 2570 compounds were called as potential kinase inhibitors by the model, and 1951 of these were specifically called as likely DDR1 compounds. The pharmacophore-based screening took these down to 848 candidates, and the group then picked 40 structures that scattered across the chemical space.

These were almost entirely outside of currently patented chemical matter at this point, and the team chose six of them for experimental validation. I tried running the resulting chemotypes through Reaxys, and it’s true, you don’t find much, as advertised. Of the six compounds, two of them hit DDR1 at around 10 and 20 nM, two others were up in the hundreds of nanomolar range, and two were completely inactive.

What I haven’t been able to find is how these six compounds scored in the evaluations above: it would be very interesting to know if the two complete misses had any distinguishing signs versus the two very solid hits, or if they were all in the same basket before testing. My guess is the latter. Overall, if I think about the (many!) kinase inhibitors I’ve seen and about DDR1 inhibitors in specific, and you show me these compounds and ask if they look like they could be added to the list of known ligands for that target, I’d say “Sure, why not?” They look perfectly reasonable (not otherwordly in any way), but of course there are heaps of other structures you could say that about, too – which is why you do screening, of course.

The results of a kinase screening panel are provided for the most potent compound, and it’s pretty good against 44 other kinases. But none of the compounds have data against any more general screens, which would be interesting to see. It would also be worth knowing how some of the reported DDR1 chemical matter looked in that same kinase screen, for that matter.

The paper goes on to do some microsomal stability assays, mechanistic cell assays, and even doses the lead compound in mice, but to tell the truth, I’m actually less interested in those parts. Metabolic stability wasn’t one of the things that the virtual screening selected for, so it’s the same crap shoot as with any other set of fresh compounds, and if a 10 nM kinase inhibitor has enough stability to be dosed and doesn’t look obviously crazy (and these compounds don’t) I assume that it will show some effects in a cell assay and indeed, even in a rodent. As this one does.

So my evaluation is that this is one of the most interesting virtual screening papers I’ve read. People have been working on virtual screening for a long time now (decades) and it’s been slowly improving the whole time. I take this paper as another step down that long road, and I’m glad that the field is moving forward. The authors do slip some of what I would call headline-bait into the last paragraph, though, saying “In this work, we designed, synthesized, and experimentally validated molecules targeting DDR1 kinase in less than 2 months and for a fraction of the cost associated with a traditional drug discovery approach“.

Ah, but how long would it take you to find DDR1 chemical matter by traditional means, if you weren’t doing anything else? Not a heck of a lot longer than that, honestly, and the costs at this point (by any technology) are but tiny little roundoff errors in the total cost of a real drug development project. I like the statement above the last paragraph a lot more: “Despite reasonable microsomal stability and pharmacokinetic properties, the compounds that have been identified here may require further optimization in terms of selectivity, specificity, and other medicinal chemistry properties“. Yes indeed, and that is where you will start to begin to commence to spend the vast piles of money that you will eventually go through in trying to get a drug to market.

Let’s also remember that DDR1 is a very well-trampled area for small-molecule drug discovery. Where would you be, for example, if you didn’t have a big ol’ list of good-quality X-ray crystal structures in order to build yourself a pharmacophore model? If you didn’t have a set of hundreds of known kinase inhibitors to help train your software on (not forgetting either that kinase inhibition is by now one of the most well-worked-out areas at a binding-model level in all of medicinal chemistry). I don’t fault the authors one bit for using this as a proving ground; that’s what you have to do with new technology. But neither should anyone overlook the fact that this example was grown in a very well-maintained greenhouse, not in a clearing hacked out of the jungle.

And that brings us to the press coverage. Oh, dear. The coverage from people who know what they’re talking about is here and here, and I strongly recommend those pieces by Ash Jogalekar and Andreas Bender, respectively. I think that they’re roughly where I am on this one: very interesting paper, with some real strengths and some real limitations, but geez, the headlines. The worst I’ve seen so far is this hyperventilating article at LinkedIn, which informs us that “This is Pharma’s AlphaGo moment when the potential for AI to radically transform the operating procedures and business models of the entire industry becomes obvious to the public” and takes off eventually into statements like “By using AI in drug development, it’s possible to accurately predict which drugs will be safe and effective for specific patient subgroups“. Why yes, this was written by someone who helped fund InSilico, why do you ask?

Let’s get this straight: this paper did not discover a drug. It discovered what might be a drug candidate, after a lot more work is done on it. (But no one’s going to do that work, because at the moment the world does not need another drug candidate for DDR1). It did this on a very well-worked-out drug target in an extremely well-studied target class, and generalizing these techniques enough to take them into new drug discovery territory is going to take a lot of time, money, and effort. To get personal, I myself am working on the sort of target that makes anybody’s virtual screening technology choke, turn purple, and fall over. We have plenty of those. So all these folks going on about huge transformative revolutions and all the rest of it should go take a cold shower or something.

The good news, though, is that there is no reason that virtual screening can’t do great things, eventually. We just have to get a lot better at it than we are now, and that’s as true as it was when I first heard about it in the mid-1980s. The academic and industrial groups that have been working on it over the decades have advanced the field a great deal, but there’s plenty more advancement needed. I liked this paper because it shows that very advancement in action, not because it heralds the end of the process. That end, folks, is not yet at hand.
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