HCovDock

HCovDock models covalent protein-ligand interactions to predict covalent inhibitor binding modes and evaluate covalent binding energies for structure-based covalent drug discovery.

Key Features:

• Efficient Docking Algorithm: Implements incremental construction ligand sampling combined with a scoring function that incorporates covalent bond-based energy.
• Benchmark Performance: Evaluated on a benchmark dataset of 207 protein-ligand complexes and compared against AutoDock, Cov_DOX, CovDock, FITTED, GOLD, ICM-Pro, and MOE.
• High Success Rate: Achieves 70.5% success for top-1 predictions and 93.2% for top-10 predictions using ligand RMSD < 2.0 Å as the criterion.
• Virtual Screening Validation: Validated by virtual screening across 10 receptors from three different proteins.
• Computational Efficiency: Reports an average runtime of ~5 minutes per ligand, ~1 second for ligands with one rotatable bond, and ~18 minutes for ligands with up to 23 rotatable bonds.

Scientific Applications:

• Covalent inhibitor design and optimization: Predicts covalent binding modes to support structure-based design and optimization of covalent drugs.
• Virtual screening for covalent binders: Supports virtual screening campaigns against multiple receptors to identify covalent hit compounds.
• Method benchmarking and comparative evaluation: Enables comparative assessment of covalent docking performance across established docking programs.

Methodology:

Uses incremental construction ligand sampling and a scoring function that explicitly includes covalent bond-based energy.