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Status: Unchecked ×Keyword: protein-ligand interactions ×Clear all

2 claims from 1 paper

1. Computer Science › Computational Drug Discovery Methods

   Efficient generation of protein pockets with PocketGen

   Zhang, Shen, Liu and Žitnik · Nature Machine Intelligence · 2024

   The authors introduce PocketGen, a deep generative model that designs the ligand-binding region of a protein, including its residue sequence and atomic structure, using a graph transformer and a protein language model.

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   1. UncheckedPocketGen is reported to run ten times faster than physics-based methods, with 97% of its generated pockets binding a ligand more strongly than reference pockets.“It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets.”
   2. UncheckedPocketGen, a model that designs ligand-binding protein pockets, is reported to recover more than 63% of amino acids when compared with reference pockets.“Additionally, it attains an amino acid recovery rate exceeding 63%.”