OpenDock

OpenDock enables protein-ligand docking and modelling by providing a Python and PyTorch-based framework that integrates multiple scoring functions and sampling strategies for flexible protein-ligand modelling tasks.

Key Features:

• Multiple Scoring Functions: The framework supports integration of multiple scoring functions, with some usable during molecular docking and pose optimization and others for post-processing scoring.
• Simulated Annealing Sampling: The current version supports simulated annealing for sampling docking poses and protein side chain orientations.
• Monte Carlo Optimization: The current version supports Monte Carlo optimization for sampling docking poses and protein side chain orientations.
• Extensible Sampling Methods: The framework can be extended to include genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations.
• Distance Constraints: Distance constraints are implemented to enable covalent docking, restricted docking, or distance map constraints guided pose sampling.

Scientific Applications:

• Computer-Aided Drug Design: The framework serves as a valuable tool in drug design by offering flexibility for protein-ligand modelling tasks.
• Enzyme Engineering: The framework serves as a valuable tool in enzyme engineering by offering flexibility for protein-ligand modelling tasks.

Methodology:

OpenDock is built on Python and PyTorch. It supports simulated annealing and Monte Carlo optimization for sampling, with extensibility for genetic algorithms and particle swarm optimization.