Deep Learning Model for Flexible and Efficient Protein-Ligand Docking

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 re-docking to an existing co-crystalized protein structure ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Here we present a deep learning model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of search algorithms obsolete. Our method introduces a new approach for the reconstruction of ligand poses in Cartesian coordinates, utilizing EDM completion and restrained energy-based optimization. The model was trained on a large-scale dataset of protein-ligand complexes and evaluated on standardized test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.