Ligand- and receptor-based docking with LiBELa.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Molecular docking methodologies are continuously evolving.
• Balancing computational cost with accurate physical descriptions of ligand-receptor interactions remains a challenge.

Purpose of the Study:

• To develop an advanced molecular docking approach.
• To improve the prediction of ligand-receptor interactions by integrating ligand- and receptor-based strategies.

Main Methods:

• Developed a hybrid docking engine combining ligand- and receptor-based strategies.
• Utilized 3D descriptors of reference ligands for initial molecule placement.
• Employed interaction energy-based global minimization with soft-core and force field potentials.
• Evaluated ligand pose and scoring.

Main Results:

• The hybrid docking approach demonstrated competitive performance against purely receptor-based methods.
• Achieved improved logAUC values on DUD and DUD-E datasets.
• Showed good reproduction of experimentally determined binding poses.
• Effectively enriched active molecules against decoys.

Conclusions:

• A combined ligand- and receptor-based docking strategy with a force field energy model enhances binding pose prediction and active molecule enrichment.
• This hybrid approach offers a valuable tool for drug discovery research.
• The developed strategy is implemented in the LiBELa software.