Ten years ago we highlighted a
paper out of Brian Shoichet’s group in which 137,639 commercially available
fragments were screened against the anti-bacterial target AmpC β-lactamase,
resulting in a couple dozen weak hits, one of which was ultimately optimized to
a picomolar covalent inhibitor. As evidenced by the devices in our pockets, computers
have improved over the past decade. This is beautifully illustrated in a paper
just published in Nature by Brian
Shoichet, Bryan Roth, John Irwin, and an international team of collaborators at
UCSF, UNC Chapel Hill, and labs in China, Ukraine, and Latvia.
Rather than limiting themselves
to commercially available compounds, the researchers turned to a virtual set of
make-on-demand molecules available from Enamine. These are built from 70,000
building blocks using 130 different two-component chemical reactions; 350
million molecules are currently available, with one billion expected by next
year. The molecules exist virtually in the ZINC database, but can be physically
ordered from Enamine as well. According to the Methods section of the paper,
93% of compounds ordered were successfully synthesized and delivered within six
weeks.
The researchers screened 99
million virtual molecules using the program DOCK3.7. On average, 280
conformations of each molecule were fit into the active site in 4054
orientations. The top million compounds were then grouped by scaffold, and only
molecules that differed considerably from known AmpC ligands and commercial compounds
were considered further. Fifty one compounds were actually made and tested, of
which five were active with affinities between 1.3 and 400 µM. Next, 90 analogs
of these were synthesized, and more than half were active; the best came in at
77 nM, among the most potent non-covalent AmpC inhibitors ever reported.
Crystal structures of several ligands from different scaffolds showed good agreement
with the docking predictions.
For a test case against a very
different binding pocket, the researchers turned to the D4 dopamine
receptor, against which they screened 138 million molecules in silico: 70
trillion different complexes, a process that took just 1.2 days using 1,500
cores. As with AmpC, the top hits were clustered, and anything resembling
commercially available or known ligands was discarded. Of 549 compounds
purchased and tested, 81 had Ki values of 8.3 µM or better. Many of
the molecules were also active in functional assays, including full and partial
agonists and even a couple antagonists. One molecule, a 180 pM agonist, was
2500-fold selective against the related D2 and D3
dopamine receptors. By way of comparison, in work published by some of the
researchers just two years ago, the best hit from 600,000 commercial compounds
was a 260 nM agonist which required three rounds of medicinal chemistry optimization
to get to 4 nM.
How well did the hit rates
correlate with the docking scores? The researchers separated the molecules
screened against the D4 receptor into a dozen “bins” and randomly
chose 444 molecules from across the bins to make and test. Happily, the hit
rates did in fact vary by score: among top bins, hit rates were 22-26%,
dropping to 12% in the middle, and 0% at the bottom. Based on these numbers
(and considerably more sophisticated analyses, including Bayesian statistics),
the researchers suggest that the library of 138 million molecules contains more
than 453,000 D4 receptor ligands in more than 72,600 scaffolds with
inhibition constants of at least 10 µM, and perhaps 158,000 with Ki
values of 1 µM or better. These may well be conservative estimates, as they
assume no hits among poorer-scoring molecules.
In a human-machine head-to-“head”
contest, the researchers chose 124 of the top-ranked molecules manually and
another 114 based on docking scores alone. Reassuringly, carbon-based systems
held out over silicon, with hit rates for both sets around 24% but the human-chosen
molecules typically having higher affinities, including the 180 pM winner. But
while human performance will likely remain steady for the near future, machines
will continue to improve.
On an academic level, the
approach described in this paper could allow empirical tests of the molecular complexity hypothesis. It would be fascinating to see whether hit rates are
higher for smaller molecules than larger ones, though of course smaller ligands
are likely to have lower affinities and are thus less likely to be among the
top hits. As in a previous analysis from Astex, one would need to compare hit
rates among molecules with equal numbers of non-hydrogen atoms.
On a practical level, ultra-large
library docking could be a game-changer for targets that have been structurally
characterized. If the method proves generalizable, the question a decade hence
may not be how to find hits, but rather how to choose between hundreds of thousands
of them.
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