Chemical Coverage of the Human Reactome
Kwak, H. A.; Liu, L.; Tredup, C.; Röhm, S.; Prinos, P.; Böttcher, J.; Schapira, M.
Show abstract
Chemical probes and chemogenomic compounds are valuable tools to link gene to phenotype, explore human biology and uncover novel targets for precision medicine. A growing federation of scientists is contributing to the mission of Target 2035 - discovering chemical tools for all druggable human proteins by the year 2035. It is expected that these compounds will enable the understanding of the regulation of cellular machineries and biological processes across the compendium of signaling pathways that animate cellular life. Here, we draw a landscape of the current chemical coverage of the human Reactome. We find that even though available chemical probes and chemogenomic compounds are targeting only 3% of the human proteome, they cover 53% of the human Reactome, due to the fact that 46% of human proteins are involved in more than one cellular pathway. As such, existing chemical probes and chemogenomic compounds already represent a versatile toolkit to manipulate a vast portion of human biology. Pathways targeted by existing drugs may be enriched in unknown but valid drug targets and could be prioritized in future Target 2035 efforts.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics 93%
- A Chemoproteomic Atlas of the Human Purine Interactome for Regioselective Ligand Discovery 93%
- Targeted protein degradation reveals BET bromodomains as the cellular target of Hedgehog Pathway Inhibitor-1 93%
Similar papers in this journal
- A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds 91%
- Protein embeddings and deep learning predict binding residues for various ligand classes 91%
- Tales of 1,008 Small Molecules: Phenomic Profiling through Live-cell Imaging in a Panel of Reporter Cell Lines 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.