Molecular docking

What is molecular docking?

Molecular docking is a computational method for generating and ranking plausible bound configurations of two molecules. The term covers small-molecule docking, macromolecular complex docking, peptide and antibody docking, global searches, flexible and ensemble protocols, and learned pose-prediction models. Docking produces hypotheses rather than direct measurements of binding or biological activity.

A docking method combines a search with a scoring or confidence model. The search explores relative position, orientation, and internal conformation; the model ranks candidate complexes using simplified physical terms, statistical potentials, or learned structural patterns. Protein–ligand methods search a small molecule in a pocket, while macromolecular methods handle larger, more flexible interfaces.

Docking is not one universal calculation. Local docking assumes a known site; blind docking searches broadly; flexible and ensemble methods represent receptor motion; consensus docking compares methods; and AI docking uses learned generative models. Choose the workflow from the molecular partners, available evidence, and uncertainty that the calculation must represent.

When to use molecular docking

• Pose generation. Build testable 3D hypotheses for how molecular partners may interact.
• Comparative modeling. Compare methods, receptor states, pockets, or partner-specific strategies.
• Structure-guided decisions. Prioritize complexes for inspection, refinement, screening, or experimental validation.

Benefits of molecular docking

• Structural hypotheses. Returns explicit complexes that can be inspected and tested.
• Method choice. Supports distinct partner classes and flexibility assumptions.
• Connected follow-up. Links pose generation to interaction, geometry, and stability review.

Primary limitations

• Approximate scoring. Docking scores and model confidence are not experimental affinity.
• Preparation sensitivity. Structures, protonation, chemistry, and search boundaries affect results.
• Biological validation. A plausible pose does not establish interaction or function.

What a molecular docking calculation actually does

Docking starts from structural representations of two molecular partners. Depending on the method, one partner may remain fixed while the other translates, rotates, and changes torsional angles, or both partners may undergo limited or learned conformational adjustment. The algorithm samples only a fraction of the possible configurations, so search settings and starting structures shape what solutions can be found.

The resulting score is an approximation designed to rank candidate configurations efficiently. AutoDock Vina reports a Vina-family energy estimate, GNINA adds learned CNN signals, macromolecular methods use their own weighted terms, and generative models return model-specific confidence. These values are not interchangeable and none is a direct experimental measurement of affinity, kinetics, occupancy, or biological effect.

• Search space. Defines where the partners may move and which ligand, side-chain, backbone, or rigid-body degrees of freedom are sampled.
• Pose generation. Produces alternative candidate complexes rather than one uniquely determined structure.
• Ranking and review. Orders candidates with a method-specific score or confidence that must be interpreted alongside geometry and independent evidence.

How to choose a molecular docking method

Start with the molecular partners. Protein–ligand methods do not automatically transfer to protein–protein, antibody–antigen, or peptide–protein complexes because the search spaces and scoring assumptions differ.

Then decide whether the site is known, how receptor flexibility should be represented, and whether multiple conformations or methods are needed. Preserve each method’s native output and validate the protocol on relevant known systems whenever possible.

• Known pocket and small molecule. Start with protein–ligand docking and a local search region supported by a co-crystal ligand, residues, or pocket evidence.
• Unknown pocket. Use blind docking for binding-site hypothesis generation, ideally with independent cavity detection and explicit false-positive review.
• Protein or peptide partner. Choose a macromolecular method designed for protein–protein, antibody–antigen, or peptide–protein interfaces.
• Conformational uncertainty. Use flexible or ensemble docking when structural evidence shows that one rigid receptor state is inadequate.
• Method uncertainty. Use consensus or AI-method comparison to expose sensitivity to scoring and learned-model assumptions.

How to do molecular docking online

ProteinIQ provides runnable templates for each maintained docking use case. The exact inputs and tools change with the partner class, but the preparation and review principles remain consistent.

1. Choose the docking type. Select the use case that matches the molecular partners, binding-site knowledge, receptor flexibility, and intended decision.
2. Prepare every structure. Check chains, missing atoms, cofactors, waters, protonation, ligand stereochemistry, charge, and the biological state represented by each input.
3. Open the matched workflow. Use the page workflow template so the selected tools, input ports, and method comparison reflect that docking problem.
4. Configure the scientific boundary. Define the pocket, restraints, flexible residues, receptor conformations, model settings, or comparison rule before running.
5. Inspect and export all evidence. Review poses, complex geometry, native scores, confidence, logs, failures, and provenance, then download structures for validation and reporting.

How the featured workflow works

A defensible workflow prepares inputs consistently, chooses a scientifically matched search strategy, runs one or more methods, and reviews geometry and uncertainty before validation.

1. Classify the system. Identify the partner class and biological question.
2. Prepare structures. Repair structures and standardize ligand chemistry.
3. Define the search. Set site, restraint, flexibility, or ensemble boundaries.
4. Generate models. Run complementary methods and preserve native outputs.
5. Validate. Inspect geometry and compare with orthogonal evidence.

Inputs and outputs for the featured workflow

Check formats before running, then inspect and download the result from every workflow step.

Inputs

Molecular structures

PDBSDFSMILES

Prepared structures appropriate to the selected partner class.

Search evidence

Binding sites, restraints, reference complexes, receptor ensembles, or known interactions when available.

Outputs

Candidate complexes

PDBPDBQTSDF

Method-native ranked poses or complex models.

Review package

Scores, confidence, contacts, logs, failures, provenance, and downloadable files.