Scaffold hopping enables direct access to more potent PROTACs with in vivo activity.
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ABSTRACT:
SUBMITTER: Burslem GM
PROVIDER: S-EPMC7404552 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:CAMKK2 is a serine/threonine kinase and an activator of AMPK whose dysregulation is linked with multiple diseases. Unfortunately, STO-609, the tool inhibitor commonly used to probe CAMKK2 signaling, has limitations. To identify promising scaffolds as starting points for the development of high-quality CAMKK2 chemical probes, we utilized a hinge-binding scaffold hopping strategy to design new CAMKK2 inhibitors. Starting from the potent but promiscuous disubstituted 7-azaindole GSK650934, a total of 32 compounds, composed of single-ring, 5,6-, and 6,6-fused heteroaromatic cores, were synthesized. The compound set was specifically designed to probe interactions with the kinase hinge-binding residues. Compared to GSK650394 and STO-609, 13 compounds displayed similar or better CAMKK2 inhibitory potency in vitro, while compounds 13g and 45 had improved selectivity for CAMKK2 across the kinome. Our systematic survey of hinge-binding chemotypes identified several potent and selective inhibitors of CAMKK2 to serve as starting points for medicinal chemistry programs.
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Project description:BACKGROUND:Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock). RESULTS:The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity. CONCLUSION:Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.
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Project description:Scaffold hopping is a central task of modern medicinal chemistry for rational drug design, which aims to design molecules of novel scaffolds sharing similar target biological activities toward known hit molecules. Traditionally, scaffolding hopping depends on searching databases of available compounds that can't exploit vast chemical space. In this study, we have re-formulated this task as a supervised molecule-to-molecule translation to generate hopped molecules novel in 2D structure but similar in 3D structure, as inspired by the fact that candidate compounds bind with their targets through 3D conformations. To efficiently train the model, we curated over 50 thousand pairs of molecules with increased bioactivity, similar 3D structure, but different 2D structure from public bioactivity database, which spanned 40 kinases commonly investigated by medicinal chemists. Moreover, we have designed a multimodal molecular transformer architecture by integrating molecular 3D conformer through a spatial graph neural network and protein sequence information through Transformer. The trained DeepHop model was shown able to generate around 70% molecules having improved bioactivity together with high 3D similarity but low 2D scaffold similarity to the template molecules. This ratio was 1.9 times higher than other state-of-the-art deep learning methods and rule- and virtual screening-based methods. Furthermore, we demonstrated that the model could generalize to new target proteins through fine-tuning with a small set of active compounds. Case studies have also shown the advantages and usefulness of DeepHop in practical scaffold hopping scenarios. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00565-5.
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