Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility

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.

Automated summary

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.