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- W4295261510 abstract "Off-target binding is one of the primary causes of toxic side effects of drugs in clinical development, resulting in failures of clinical trials. While off-target drug binding is a known phenomenon, experimental identification of the undesired protein binders can be prohibitively expensive due to the large pool of possible biological targets. Here, we propose a new strategy combining chemical similarity principle and deep learning to enable proteome-wide mapping of compound-protein interactions. We have developed a pipeline to identify the targets of bioactive molecules by matching them with chemically similar annotated “bait” compounds and ranking them with deep learning. We have constructed a user-friendly web server for drug-target identification based on chemical similarity (DRIFT) to perform searches across annotated bioactive compound datasets, thus enabling high-throughput, multi-ligand target identification, as well as chemical fragmentation of target-binding moieties." @default.
- W4295261510 created "2022-09-12" @default.
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- W4295261510 date "2022-01-01" @default.
- W4295261510 modified "2023-09-30" @default.
- W4295261510 title "Whole proteome mapping of compound-protein interactions" @default.
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- W4295261510 doi "https://doi.org/10.1016/j.crchbi.2022.100035" @default.
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