期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 63, 期 6, 页码 1695-1707出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.2c014361695J
关键词
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Protein-ligand docking is crucial in drug design, but current docking programs often neglect protein flexibility. To overcome this challenge, we propose a deep learning model based on the prediction of intermolecular Euclidean distance matrix (EDM), eliminating the need for iterative search algorithms. The model, trained on a large-scale dataset, outperforms comparable methods, generating high quality poses for diverse protein-ligand structures.
Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimiza-tion. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.
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