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Deciphering DEL pocket patterns through contrastive learning
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DOI：10.1038/s41467-026-69663-y.png)
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Abstract
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DNA-encoded libraries (DELs) facilitate high-throughput screening of trillions of molecules against protein targets through split-pool synthesis and DNA tagging. Despite their potential, only a few DEL-derived compounds have advanced to clinical trials or reached the market. A better understanding of the defining characteristics of target proteins, particularly those with binding pockets suitable for DEL screening, is critical to improving success rates. However, existing approaches remain limited in assessing pocket flexibility and functional similarity. Here, we present ErePOC, a pocket representation model based on contrastive learning with ESM-2 embeddings to address these challenges. ErePOC captures both structural and functional features of binding pockets, enabling identification of shared characteristics among DEL targets. By integrating analyses of low-dimensional physicochemical properties and high-dimensional ErePOC embeddings, we provide a comprehensive view of DEL target space. With 98% precision in downstream classification tasks, ErePOC demonstrates high performance in pocket representation, which is then applied to predict human proteins suitable for DEL screening, with enrichment uncovered across 18 functional categories. This work establishes a framework for enhancing DEL-based drug discovery through more effective target selection and pocket similarity analysis. DNA-encoded libraries (DELs) hold promises for drug discovery but often underperform due to limited understanding of target compatibility. Here, authors present ErePOC, a pocket representation model that identifies protein pockets suitable for DEL screening.
Keywords:
Computational biophysics
High-throughput screening
Protein function predictions
Structural biology
Science
Humanities and Social Sciences
multidisciplinary
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