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Contrasting drugs from decoys
Samuel Sledzieski · Rohit Singh · Lenore J Cowen · Bonnie Berger

@
in Machine Learning in Structural Biology Workshop »

Protein language models (PLMs) have recently been proposed to advance drug-target 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.

Author Information

Samuel Sledzieski (Massachusetts Institute of Technology)
Rohit Singh (MIT)
Lenore J Cowen (Tufts University)
Bonnie Berger (MIT)

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