secretagentmoof

Posted by Derek Lowe

http://blogs.sciencemag.org/pipeline/archives/2019/02/14/odd-peroxides-indeed

http://blogs.sciencemag.org/pipeline/?p=14184

You know, normally when you start combining interesting or reactive functional groups in the same molecule, you end up with something that’s worse than before. Would I pick up a flask containing a compound that has both a perchloryl ester and a geminal di-azide? I would not, and neither should you, should someone ever be foolhardy enough to prepare such a thing. Those are only for lobbing at hostile monsters in video games. So I was interested to see a recent paper on perfluoroalkyl peroxides. Changing the electronics of that group would surely have an effect, but of what kind? (And perfluoroalkyls want all the electron density, so you know that something is going to change).

Well, to my surprise, the result is the friendliest batch of alkyl peroxides that you’re ever going to see. There was some foreshadowing, though. It turns out that the only member of this compound class that had really been characterized in the literature to date was the bis-trifluoromethylperoxide, which dates back to 1933. It was found to be weirdly stable, as in up to 200 degrees C. Do not, under any circumstances, try heating up a regular dialkyl peroxide in such a manner – you’re not going to get very far, although the pieces of the reaction vessel will.

Preparing these things is worse than handling them once they’re made, by far. As this paper shows, the previous syntheses relied on rather spirited reagents like chlorine trifluoride, and when your prep calls for that one, it’s time to re-examine the life choices that have brought you to that point. That’s what this Berlin group did, clearly, coming up with a route that involves some generally unappealing chemistry, such the treatment of silver wool with elemental fluorine gas at 100 degrees. OK, you don’t start at that temperature, no matter how much of a buckaroo you are – they introduce “small portions” of fluorine to the silver in a sealed metal apparatus at RT “until the pressure remains constant”, that is to say, until it stops whooping and hollering in there, and then they raise the temperature. You also have to prepare hypofluorites of the alkyl groups, which you do from the alcohol and cesium fluoride, introducing (yet again) elemental fluorine in portions until all the overpressures stop spiking, and then do trap-to-trap distillation at -150C. Treating the hypofluorite with that silver fluoride/silver wool, followed by another trap-to-trap purification, gives you the desired perfluoroalkyl peroxide.

None of this am I lining up to perform. Straight fluorine has long been on my “Things I Won’t Work With” list, although if I had to choose, I would go for that over using chlorine trifluoride, albeit with profound unhappiness and thoughts of chucking the whole business and opening a taco truck instead.But at the end of all this, the compounds you get out are, as mentioned, surprisingly tame. The bis(perfluoro-t-butyl) peroxide, for example, melts at about 18C, so at most room temperatures it’s colorless liquid that just sits there, bizarrely. It’s insensitive to shock, insensitive to friction, and you can even get a boiling point on it (99C), albeit with decomposition.

You cannot say any of that about the plain bis-alkylperoxides, that’s for damned sure, except for the decomposition part, which unfortunately can happen with fearsome speed when you so much as look at them funny. So what’s going on? The paper notes that the O-O bond energy isn’t that different in the two sorts of compounds. What changes, though, is the energetics of the alkoxy radicals that form when that bond cleaves. The reactions of the perfluoroalkoxy species are slow and endothermic; the reactions of the regular alkyls, definitely not. The paper goes into a number of structural and computational details – there have been some longstanding arguments about peroxide conformations and bond angles versus theoretical predictions, and these compounds have some bearing on that.

So there you have it: if you’re willing to walk through the valley of elemental fluorine, you can arrive at these odd compounds. It’s be interesting to see what sort of reactivity they have as selective oxidants. Who knows, they may turn into familiar reagents if they do something useful, or they may just remain as chemical curiosities. They’ll always be that!

http://blogs.sciencemag.org/pipeline/archives/2019/02/14/odd-peroxides-indeed

http://blogs.sciencemag.org/pipeline/?p=14184

Posted by Derek Lowe

http://blogs.sciencemag.org/pipeline/archives/2019/02/11/virtual-screening-as-big-as-it-currently-gets

http://blogs.sciencemag.org/pipeline/?p=14172

This new paper on “ultra-large” virtual screening is well worth a look in detail. We find a great many lead compounds in this business by random screening of compound libraries, and virtual screening is (as the name implies) the technique of doing this computationally instead of with hundreds (thousands) of sample plates and tireless robot arms. All of that takes time and effort and money – accumulating such a compound collection, making sure that those compounds are (or are still) what you think they are, dispensing them in a useful form, coming up with an assay that’s strong enough to run in automated fashion and actually getting it done, etc. The idea of doing all this computationally by docking mathematical representations of molecules into mathematical representations of your target has always been appealing, and it gets more so every year as the hardware gets ever more capable.  Even if you can’t predict de novo the compounds that will do the job, and we can’t, you can still run huge numbers of them, all varieties, and see which ones come out on top.

This paper (a large multi-center academic collaboration) reports what I believe is the largest publicly disclosed effort of this type. It takes as its starting point 70,000 commercially available building block compounds, and elaborates those using a set of 130 known reactions. This gives you what should be a “make-on-demand” library whose actual synthesis has a good chance of being reasonable. The paper itself screens 99 million compounds against one target (the AmpC enzyme) and 138 million against another (the D4 receptor), and the library has grown much larger since then. Less than 3% of that library is itself commercially available; there are a lot of compounds to make in this world.

The computational screening of this set is not a trivial exercise – to their credit, the authors did a pretty thorough job. You could play the game of run-a-quick-minimization-and-dock-that-as-if-were-rigid on these things, and you’d get through them pretty quickly, but to what end? Reality is more various than that. So for each compound, an average of around 4,000 orientations was checked (basically, which part of the molecule approaches the protein target and a what angle) and for each of those, 280 conformations of the molecule were sampled. That adds up to a number of possibilities in the ten to the thirteenth range, scored with DOCK3.7, which will take you tens of thousands of core hours to chew through on typical hardware. Compounds resembling known ligands for these targets were deliberately filtered out in a search for new chemical matter.

Now we get to some interesting numbers. From the AmpC hits, the team picked out 51 top-ranking molecules (each from a different scaffold class) to synthesize, and 44 of those efforts were successful. (These molecules, as with the D4 example coming up next, were selected both by docking scores and by human inspection – see below!) Of those 44, only five showed any activity in the enzyme assay (ranging from about 1 micromolar to about 400 micromolar). The best of that list represents a very good starting point indeed for this enzyme, and synthesizing analogs of its structure led to a 77 nM compound, which appears to be among the most potent non-covalent inhibitors reported for it. A crystal structure of the inhibitor/enzyme complex confirmed the predicting docking pose, which is always good to see.

As for the dopamine D4 effort, this one went a bit more in-depth. The team selected 589 structures, and not just from the top rank of the docking scores, but from the middle and lower parts of the list as well. 549 of these could be synthesized, and 122 of these showed more than 50% radioligand displacement at 10 micromolar. 81 of these were dose-responsed, and showed Ki values of 18 nanomolar to 8 micromolar. Not bad! Most of the potent compounds were full or partial agonists, but there were two antagonists in there as well. One of the potent agonists was synthesized as its four separate diastereomers, and one of those was down to 180 picomolar activity, 2500x selective against the related dopamine receptor subtypes, which is about as good as you’re ever going to get.

There are a lot of interesting take-aways from this work. For one thing, as the authors mention, it would be tempting to just dock representative members of each structural type/cluster, rather than having to do them all. But trying that really demolished the effectiveness of the screen, shedding active hits at an alarming rate. The current docking/scoring technology can get you as far as “compounds that look kind of like this”, but definitely cannot reach in and pick out the best representative of any given class. And even that level of discrimination comes with a lot of effort – note the number of hit compounds in both the examples above that turned out to be completely inactive on synthesis. That definitely argues for setting up these virtual libraries according to expected ease of synthesis, because otherwise you could spend a lot of time making tough compounds that don’t do anything. People have.

This also speaks to the importance of size. The D4 receptor has been the subject of virtual screening before, but not at this scale, and the best compounds here were (of course) not found in those efforts. Nor were any that were as potent as the best ones here. Size matters, and since we can’t zero in on the best compounds, we’d better be prepared to evaluate as many of them as we can stand.

Another point is that high-middle-low effort on the D4 case. The binding assay results compared to the docking scores are shown at right. You can see that the number of potent compounds (better than 50% displacement, below that dashed line) decreases as the scores get worse; the lowest bin doesn’t have any at all. But at the same time, there are a few false-negative outliers with binding activity at pretty low scores, and at the other end of the scale, the top three bins look basically undistinguishable. So the broad strokes are there, but the details are of course smeared out a bit.

There’s also a human-versus-machine comparison in evaluating the hits. The authors took the top 1,000 compounds and selected 124 of them by eyeballing them for what looked like good interactions in the docking pose (not looking at the scoring), and took 114 molecules on the basis of docking scores alone. The hit rates for the two sets were almost identical (about 24%), but the human-selected ones were disproportionately potent – and indeed, in the two campaigns, the human-selected compounds were quite over-represented in the lists of potent compounds. So we have that going for us. But again, note that three-quarters of the compound selected, even after all this effort, were not active. That’s a huge enhancement over background, which is good news, but it’s not the magic that some outside the field think we can work, either.

One thing to note is that these two binding sites are very well characterized. There are plenty of compounds known for each, and there’s a lot of understanding about the structure of them bound to the proteins. Trying this against a blue-sky binding site that you don’t know much about is going to be a much different undertaking – but that, of course, is what we’d like to do. Ideally computational screening will eventually do even more not only with compounds that aren’t yet real, but do that against proteins that have never been physically screened before at all. Getting solid, actionable protein structures, though, is far more difficult than running through orientations and conformers for small molecules – as it stands now, screening  modeled compounds against real protein structures can (as this paper shows) give you good results, although keep in mind that this report is pretty much at the edge of what we can do with current technology. But screening modeled compounds against modeled proteins runs a substantial risk of giving you a lot of noise. We’ll get there, but we aren’t there yet.

One other sobering note: this paper, as so many virtual screening papers do, starts off by mentioning the estimate for small-molecule chemical space of perhaps 10⁶³ compounds. There’s room to wonder about that estimate, but since it’s cited here, let’s use it against the paper’s figure of 1.2 calendar days of straight processing time on 1,500 cores to get through the 138 million compounds in the second set. Extrapolating to the Big Set of Everything That Exists, that gives us 10⁵⁵ days of processor time to screen the lot. Unfortunately, it’s only been about 5 x 10¹² calendar days, more or less, since the Big Bang. So even if we allow ourselves more time by turning each day since the universe began into another universe’s current age worth of time (setting off a Big Bang every morning and waiting another 13.8 billion years until counting the next day) and then take each day of that unimaginable stretch and turn every one of those into another universe’s-age worth of time, then you would only need around a quadrillion of those extra-long intervals to get through the data set. Large numbers are indeed large.

http://blogs.sciencemag.org/pipeline/archives/2019/02/11/virtual-screening-as-big-as-it-currently-gets

http://blogs.sciencemag.org/pipeline/?p=14172