diffdock

Overview of diffdock skill

What diffdock is for

DiffDock is a docking-focused skill for predicting protein-ligand binding poses from a protein structure or sequence plus a ligand input. Use the diffdock skill when you need a practical answer to “where and how might this compound bind?” rather than a binding-affinity estimate.

Best fit and decision boundary

It fits structure-based drug design, virtual screening, and pose generation for downstream analysis. It is a weaker fit if you only need ranking by potency, if your protein target is highly flexible, or if you want a generic chemistry workflow instead of a pose-prediction workflow.

What makes it useful

The main value is that diffdock combines single-complex docking, batch screening, confidence scoring, and sequence-based protein input in one workflow. That makes diffdock install worthwhile when you want both an executable docking path and enough guidance to avoid misreading the scores.

How to Use diffdock skill

Install and inspect the workflow

Install the diffdock skill in your Claude skills setup, then open SKILL.md first. After that, read references/workflows_examples.md, references/parameters_reference.md, and references/confidence_and_limitations.md to understand the actual input shapes, defaults, and score interpretation before running a job.

Turn your task into a usable prompt

For diffdock usage, give the skill the protein format, ligand format, and job type up front. Good input is specific, for example: “Dock this SMILES to this PDB and return the top 5 poses with confidence interpretation,” or “Prepare batch docking for these ligands against one receptor.” Weak input is just “run diffdock,” because it hides whether the skill should use a file, a sequence, or a CSV batch.

Use the right files and outputs

For single docking, start with a protein PDB and a ligand in SMILES, SDF, or MOL2. For batch work, use the CSV template in assets/batch_template.csv and check scripts/prepare_batch_csv.py if you need validation before execution. After a run, scripts/analyze_results.py helps summarize pose ranks and confidence scores so you do not manually inspect every output file.

Practical setup tips

DiffDock install and first run can be slowed by model weights and lookup-table generation, so plan for that setup cost. If your protein is not available as a structure, the skill supports sequence-based folding, but that adds uncertainty; use it when no experimental structure exists, not as a default shortcut. Adjust sampling only when the task is hard, because more samples improve search coverage but also increase compute and post-processing work.

diffdock skill FAQ

Is diffdock only for PDB files?

No. The diffdock skill supports protein structures and, in some workflows, protein sequences that are folded before docking. It is still best to use an actual PDB when you have one, because sequence-derived structures add another source of error.

Does diffdock predict affinity?

No. DiffDock predicts binding poses and confidence, not binding affinity. If you need affinity-like prioritization, pair diffdock with a scoring or rescoring step instead of treating confidence as potency.

Is the diffdock skill beginner friendly?

Yes, if your job is straightforward: one receptor, one ligand, one pose question. It becomes harder when you need batch curation, flexible proteins, or careful interpretation of low-confidence samples. The skill is beginner-friendly for docking, not for replacing domain judgment.

When should I not use it?

Do not rely on diffdock for targets where conformational change is the main binding mechanism, or when you only have a very uncertain ligand representation. It is also a poor substitute for a full medicinal chemistry analysis workflow if your real question is SAR, selectivity, or ADMET.

How to Improve diffdock skill

Give the skill better molecular context

The strongest diffdock results usually come from clean inputs: a correct receptor file, a ligand with a known protonation assumption, and a clear definition of the binding problem. If the site is known, say so. If it is a blind docking task, say that too, because the search strategy and expected confidence differ.

Ask for the output you will actually use

Improve diffdock usage by specifying whether you want the top pose, top 5 poses, batch screening, or confidence-ranked candidates. If you plan to compare results later, ask for consistent file naming and a summary table. This reduces ambiguity and makes the output easier to integrate into analysis for Data Analysis or screening reports.

Watch the common failure modes

The most common mistakes are treating confidence as affinity, using poor ligand preparation, and overtrusting runs on proteins outside the model’s comfort zone. If results look unstable, rerun with more samples, compare multiple top poses, and inspect whether the ligand chemistry or protein state is the actual blocker rather than the model.

Iterate with targeted follow-up prompts

After the first run, improve the next diffdock prompt with the specific problem: bad site placement, inconsistent pose clustering, or low confidence scores. That is more useful than asking for a generic rerun. When you need diffdock for Data Analysis, include the metric you want extracted from outputs, such as rank distribution, score thresholds, or per-complex summaries.