Fragment growing via virtual synthesis and screening

Practical Fragments has covered virtual screening for nearly ten years, and the tools continue to improve. More recently, researchers are using computational approaches not just to dock libraries of molecules, but to decide what compounds to make. The latest example, called AutoCouple, is described in an ACS Cent. Sci. paper by Cristina Nevado, Amedeo Caflisch, and colleagues at University of Zurich.

The researchers have a standing interest in bromodomains, epigenetic “reader” proteins that bind acetylated lysine residues. In particular, they were interested in CBP (cyclic-AMP response element binding protein). Previously the researchers had identified compound 1 through virtual screening, but although this compound had sub-micromolar affinity, it showed no cell-based activity, presumably due to the carboxylic acid, a moiety usually associated with poor cell permeability. Indeed, a CBP series we discussed earlier this year that also contained a carboxylic acid had no cellular activity.

To come up with better molecules the researchers used a program they developed and named AutoCouple because it virtually “grows” a fragment using common coupling reactions such as amide formation, Buchwald-Hartwig amination, and the Suzuki-Miyaura reaction. An initial set of 270,000 commercial compounds was computationally filtered to remove large molecules and those containing undesirable moieties. Potentially self-reactive building blocks were also removed. Ultimately 70,000 virtual compounds based on growing compound 2 (the key fragment of compound 1) were designed and docked into multiple crystal structures of CBP, and 53 were actually synthesized and tested.

Four of the 33 amides synthesized were sub-micromolar, compound 5 being one example; another 17 were low micromolar. (Five of the 10 Suzuki-derived compounds were also sub-micromolar, as was at least one of the amines.) Compound 5 was improved by using information from one of the other tested molecules to generate compound 16, with low nanomolar affinity. Crystallography confirmed that this compound binds as the docking had predicted, in a similar manner to compound 1.

Happily, not only was compound 16 more potent than compound 1, it was also active in cells. Moreover, it showed reasonable selectivity against a dozen other bromodomains.

Overall AutoCouple looks like it could be a useful tool to design and prioritize compounds for synthesis. Moreover, like the growing via merging “PINGUI” approach we highlighted earlier this year, the Python scripts appear to be freely available. It would be fun to benchmark both methods on the same targets to see how they compare.