SkeleDock

SkeleDock models binding poses of scaffold analogs by template-guided scaffold docking using the structural framework of existing protein-ligand complexes to support drug discovery.

Key Features:

• Template-guided scaffold docking: Uses the structural framework of an existing protein-ligand complex as a template to model binding modes of chemically similar ligands.
• Crystallized fragment exploitation: Incorporates crystallized fragments of target ligands to improve pose prediction accuracy.
• Pose prediction performance: Demonstrated competitive performance in the D3R Grand Challenge 4 pose prediction challenge.
• Macrocycle modeling: Addresses docking challenges posed by macrocycles (large ring-shaped molecules).
• Scaffold hopping capability: Facilitates scaffold hopping to identify chemically distinct compounds with similar biological activity.
• Comparative benchmarking: Outperformed rDock in specific scenarios when crystallized fragments were available.

Scientific Applications:

• Pose prediction: Predicting ligand binding poses for scaffold analogs and chemically similar systems.
• Lead identification in drug discovery: Supporting identification of novel therapeutic candidates via template-guided docking.
• Macrocycle docking: Modeling binding modes of macrocyclic compounds that pose unique docking difficulties.
• Scaffold hopping-driven hit finding: Enabling discovery of chemically distinct active compounds to address patent barriers or adverse side effects.

Methodology:

Template-guided scaffold docking that leverages the structural framework of existing protein-ligand complexes and uses crystallized ligand fragments to model ligand binding modes.