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- W4308304021 abstract "Abstract Protein language models (PLMs) have recently been proposed to advance drugtarget interaction (DTI) prediction, and have shown state-of-the-art performance on several standard benchmarks. However, a remaining challenge for all DTI prediction models (including PLM-based ones) is distinguishing true drugs from highly-similar decoys. Leveraging techniques from self-supervised contrastive learning, we introduce a second-generation PLM-based DTI model trained on triplets of proteins, drugs, and decoys (small drug-like molecules that do not bind to the protein). We show that our approach, CON-Plex , improves specificity while maintaining high prediction accuracy and generalizability to new drug classes. CON-Plex maps proteins and drugs to a shared latent space which can be interpreted to identify mutually-compatible classes of proteins and drugs. Data and code are available at https://zenodo.org/record/7127229 ." @default.
- W4308304021 created "2022-11-10" @default.
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- W4308304021 date "2022-11-04" @default.
- W4308304021 modified "2023-09-25" @default.
- W4308304021 title "Contrasting drugs from decoys" @default.
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- W4308304021 doi "https://doi.org/10.1101/2022.11.03.515086" @default.
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