Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.
J Chem Inf Model
; 63(6): 1695-1707, 2023 03 27.
Article
em En
| MEDLINE
| ID: mdl-36916514
ABSTRACT
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 optimization. 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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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article