‘Virtual Pharmacology’ Advance Tackles Universe of Unknown Drugs
ucsf.edu
ucsf.edu
http://blogs.sciencemag.org/pipeline/archives/2019/02/11/vir...
>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.
These methods can filter millions of compounds down to hundreds, but as an academic lab, it's still a herculean effort to synthesize hundreds of compounds. And out of those hundreds, you might get a couple that are active. This study is a combination hard work, yes, but also a lot of money and luck.
That being said, good for the team, and good for science. I have nothing but respect for Shoichet and Roth. Didn't ever cross paths with Irwin.
This makes me laugh since Shoichet was childhood friends with Irwin and they've worked together on almost everything together since 2000 when Irwin went to Northwestern to join his lab.
Many of the questions in virtual screening of large libraries are not answered in the paper as the authors did not look at them. As mentioned in the comments, they are using Enamine RealDB which has been around for a while, and Enamine has an even larger virtual library called RealSpace.
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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.
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Are we in a situation now where, if I have a bad anti-botic resistant infection I can just order these molecules on the off chance that they will help me?
Can I order a toxin?
Or are these molecules just impractical to use outside of a lab setting
This is the very first step towards developing potential drugs, it's very, very far from an actual drug. And drug development wasn't the goal of this paper anyway.
And the idea behind this paper was to find molecules that aren't in any catalogue, so you would still have to synthesize them yourself or pay someone to do a custom synthesis for you.
> Of the 589 molecules selected, 549 (93%) were successfully synthesized (Supplementary Table 10 and Supplementary Data 11, 13)
I fully agree your main points.
> Over the past decade, Kiev-based Enamine Ltd has innovated an efficient pipeline to produce any of over a billion never-before-made drug-like compounds on demand — at a cost of about $100 per molecule — by combining any of tens of thousands of standard chemical building blocks with one another using over a hundred established chemical reactions.
E.g. here is one of them:
Very far from it. I wrote a docking program myself (the most popular one, currently, I believe), and I get emails of this nature sometimes.
Molecular docking only makes predictions about binding. Typically, this gives you what people call an "enrichment", i.e. your guesses are better than random, hopefully much more so. Still, most of these guesses are typically wrong. For example, you might go from 1 in 1000 odds to 1 in 10.
When you do find a drug-like molecule that binds your chosen protein, this is only the beginning of the drug development effort that may end up costing billions.
They could likely simulate tons more, but there is no guarentee that they are; Actually synthesizable, actually hit the target, aren’t toxic.
So at some point the current computing capabilites fall shot. But not because we can’t throw more cpu hours at the problem, simply because we don’t have the computational tools to cross those bery important barriers available at all.
Using more CPU time in this would have helped a lot.
The good news is that once the library is prepared, it is quick to screen at more targets--and we make the pre-computed library available at zinc15.docking.org.
Interestingly, as the library grows a limiting factor is storing the library on disk. It is now ~20T. We've set up several mirrors around the world for groups that are actively using it. An interesting problem will be to see if preparing compounds for screening on the fly (e.g. with machine learning models) can overcome this limitation to keep up with library growth.
A big question for us is what will the return on investment in screening larger and larger libraries be? One of the take aways from this work is if docking has moderate enrichment, than screening larger libraries not only gives more hits but actually can increase the hit-rate for the top scoring compounds.