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bioRxiv · 10.1101/2022.11.03.515086

Contrasting drugs from decoys

Sledzieski, S.·Singh, R.·Cowen, L.·Berger, B.
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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.

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Sledzieski, S., Singh, R., Cowen, L., Berger, B.. 2022-11-04. Contrasting drugs from decoys. https://doi.org/10.1101/2022.11.03.515086

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