Trends in Biochemical Sciences综述丨大规模靶向“不可成药”蛋白的全新策略
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TL;DR - A Trends in Biochemical Sciences perspective proposes large-scale chemoproteomics on native cellular proteins to discover ligands for conventionally “undruggable” targets. Combining proteome-wide interaction maps with AI could expand drug discovery beyond proteins accessible to structure-based methods.
- Fewer than 12% of proteins are targeted by approved drugs, while only about 35% of the human proteome has experimentally resolved structures.
- Cellular assays preserve post-translational modifications, protein interactions, oligomers, aggregates, and condensates that can reshape ligand-binding opportunities.
- AI could denoise ligand–protein matrices, identify binding fingerprints, and predict unmeasured interactions or binding regions.
- Key limitations include assay bias, scarce high-quality labeled data, nonspecific binding, and the need for functional and structural validation.
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Trends in Biochemical Sciences综述丨大规模靶向“不可成药”蛋白的全新策略
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OpenAlex citations 98 · Semantic Scholar citations 93 · Semantic Scholar influential citations 5
TL;DR - A Trends in Biochemical Sciences perspective proposes large-scale chemoproteomics on native cellular proteins to discover ligands for conventionally “undruggable” targets. Combining proteome-wide interaction maps with AI could expand drug discovery beyond proteins accessible to structure-based methods.
- Fewer than 12% of proteins are targeted by approved drugs, while only about 35% of the human proteome has experimentally resolved structures.
- Cellular assays preserve post-translational modifications, protein interactions, oligomers, aggregates, and condensates that can reshape ligand-binding opportunities.
- AI could denoise ligand–protein matrices, identify binding fingerprints, and predict unmeasured interactions or binding regions.
- Key limitations include assay bias, scarce high-quality labeled data, nonspecific binding, and the need for functional and structural validation.