docking
Definition
Docking is a computational method that predicts the preferred orientation and binding affinity of a small molecule (ligand) when it binds to a target protein or nucleic acid receptor. This structure-based approach evaluates millions of potential binding poses by calculating interaction energies, geometric complementarity, and conformational flexibility. Docking algorithms use scoring functions to rank predicted binding modes, helping identify promising drug candidates and understand molecular recognition mechanisms. It's fundamental in structure-based drug design, enabling researchers to screen virtual compound libraries against therapeutic targets before costly experimental validation. Docking results inform lead optimization by revealing key binding residues, interaction types (hydrogen bonds, hydrophobic contacts), and potential off-target effects.
Visualize docking in Nodes Bio
Researchers can visualize docking results as networks where nodes represent proteins, ligands, and binding sites, with edges showing predicted interactions and binding affinities. Network analysis reveals polypharmacology patterns, off-target binding profiles, and structure-activity relationships across compound libraries. Users can map docking scores onto protein-protein interaction networks to identify druggable nodes and visualize how small molecules might modulate pathway activity through multi-target engagement.
Visualization Ideas:
- Compound-target interaction networks showing docking scores as edge weights
- Multi-target binding profiles revealing polypharmacology and selectivity patterns
- Protein pocket-ligand networks mapping binding site residues to chemical features
Example Use Case
A pharmaceutical team investigating kinase inhibitors for cancer therapy performs docking studies of 10,000 compounds against 50 kinase structures. They discover that their lead compound shows strong predicted binding to both the intended target (EGFR) and three off-target kinases. By visualizing these multi-target docking results as a compound-protein interaction network, they identify structural features causing promiscuity and design selective analogs. The network reveals that compounds binding a specific pocket region show improved selectivity, guiding medicinal chemistry optimization.