What will you do with hundreds of thousands of new ligands?

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 K_i 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 K_i 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.