molecular-docking
Skill62 starsChanged 5 months agoMIT-0View on GitHub
Molecular docking pipeline using AutoDock Vina for structure-based drug discovery. Triggers on docking, AutoDock Vina, receptor preparation, ligand preparation, PDBQT, grid box, virtual screening, binding affinity, pose prediction, structure-based virtual screening, "redocking RMSD", "Vina score", "docking pose", "prepare receptor", "ligand library screening".
1 other project uses a version of this.
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Most recently taken up by
- SciMiner/sciminer-skillstheir own version, first seen Aug 18, 2026
Other versions1 project uses a version of this.
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Wrote their own version · 1
- SciMiner/sciminer-skills★ 18same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository41 other files it gives its agents.
- aws-genai-ml-architectSkill
- bioinformatics-on-healthomicsSkill
- biomarker-discoverySkill
- cdisc-complianceSkill
- cell-type-annotationSkill
- cheminformaticsSkill
- claims-analyticsSkill
- claims-billing-rulesSkill
- clinical-data-standardsSkill
- coordination-of-benefitsSkill
- dicom-processingSkill
- digital-pathologySkill
- drug-repurposingSkill
- edc-data-validationSkill
- ehr-data-parsingSkill
- genomic-variant-interpretationSkill
- hedis-measure-specificationSkill
- imaging-study-designSkill
- medical-device-software-complianceSkill
- ml-researcherSkill
- multi-omics-integrationSkill
- multi-omics-pipelineSkill
- ngs-quality-controlSkill
- pa-clinical-policySkill
- pa-decision-automationSkill
- pharmacoepidemiologySkill
- protein-structure-analysisSkill
- provider-denial-workupSkill
- quality-measuresSkill
- quantitative-proteomicsSkill
- radiology-preprocessingSkill
- risk-adjustmentSkill
- risk-adjustment-strategySkill
- risk-stratification-indicesSkill
- rna-seq-analysisSkill
- rwd-cohort-analysisSkill
- scrna-seq-pipelineSkill
- structure-based-drug-designSkill
- trajectory-analysisSkill
- translational-researchSkill
- variant-callingSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 78
Right: Use PROPKA or H++ for acidic/basic pockets
- Very specific: line 85
Wrong: Default exhaustiveness = 8 for large/flexible ligands
- Distinctive: line 86
Right: Use 32+ for production; 64+ for >8 rotatable bonds
- Very specific: line 94
Right: Use prepare_receptor (ADFR) or meeko for production
- Distinctive: line 102
Right: Only compare within same receptor + grid.
- Distinctive: line 110
Keep code comments minimal and functional (what, not why-it-exists)
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Molecular Docking — Pipeline Skill (Thin Scaffold)
Overview
Adds decision logic for receptor/ligand preparation tool selection, grid box sizing, exhaustiveness tuning, and validation protocol. Focuses on gotchas that produce silently wrong poses.
Usage
- Activate when choosing preparation tools (obabel vs ADFR/meeko) for a docking campaign
- Activate when defining grid box dimensions or placement
- Activate when validating docking setup via redocking
Core Concepts
Decision Logic
Receptor preparation tool:
├── Quick exploration / single ligand → obabel (-d, reduce, -xr)
└── Production virtual screen → prepare_receptor (ADFR) or meeko
└── Handles altlocs, metals, non-standard residues correctly
Ligand preparation:
├── Single ligand from SMILES/SDF → obabel --gen3d -h
├── Large library (>100 ligands) → meeko (mk_prepare_ligand.py)
└── Stereocenters undefined? → Enumerate explicitly BEFORE docking
Grid box placement:
├── Co-crystal ligand available → Center on ligand centroid
└── No co-crystal → Use pocket predictor (fpocket, P2Rank, SiteMap)
Grid box sizing:
└── Ligand longest diameter + 10–15 Å padding per axis
└── Typical: 20–30 Å per side for drug-like molecules
Exhaustiveness:
├── Quick exploration → 8 (Vina default)
├── Production / ranking → 32
└── Large/flexible ligands (>8 rotatable bonds) → 64+Validation gate (MUST pass before trusting screen results): Redock co-crystal ligand → RMSD < 2 Å against crystallographic pose. If fails, fix preparation or grid before screening.
Critical Parameters
| Parameter | Recommended | When to change |
exhaustiveness | 32 | 64+ for >8 rotatable bonds |
num_modes | 10 | Increase for ensemble analysis |
energy_range | 3 kcal/mol | Widen to capture diverse poses |
| Box padding | +10–15 Å | Larger for allosteric sites |
| Protonation pH | 7.0 (reduce default) | Use PROPKA/H++ for acidic pockets (e.g., aspartyl proteases pH 4.5) |
Score interpretation:
- Vina scores are only comparable within the SAME receptor + grid setup
- Typical drug-like hits: -7 to -12 kcal/mol
- Scores are NOT transferable across different targets or grid configurations
Common Mistakes
- Wrong: Leaving crystallographic waters in the receptor Right: Strip HOH/WAT residues before PDBQT conversion (unless explicit-water docking) Why: Waters block the binding site and produce unphysical poses
- Wrong: Using default pH 7 protonation for all binding sites Right: Use PROPKA or H++ for acidic/basic pockets Why: Wrong protonation alters H-bond networks and produces incorrect poses
- Wrong: Grid box barely enclosing the binding site Right: Ligand diameter + 10–15 Å padding per axis Why: Under-padding clips the site and biases poses toward box center
- Wrong: Default
exhaustiveness = 8for large/flexible ligands Right: Use 32+ for production; 64+ for >8 rotatable bonds Why: Low exhaustiveness misses global minima, unreliable rankings - Wrong: Skipping redocking validation before screening Right: Redock co-crystal ligand, verify RMSD < 2 Å Why: RMSD ≥ 2 Å means preparation or grid is wrong; screen results untrustworthy
- Wrong: Using
obabel -xrPDBQT for production virtual screens Right: Useprepare_receptor(ADFR) or meeko for production Why: obabel may miss altlocs, metals, or non-standard residues - Wrong: Running
obabel --gen3don SMILES with undefined stereocenters Right: Provide isomeric SMILES or enumerate stereoisomers explicitly Why: Undefined stereocenters resolved arbitrarily — may dock wrong enantiomer - Wrong: Comparing Vina affinities across different receptors or grid configs Right: Only compare within same receptor + grid; re-rank when changing either Why: Scores are context-dependent, not transferable
Response Format
- Lead with the command or code the user needs — explain after
- Structure as: confirm inputs → working code → key parameters explained → gotchas
- One complete working example per task; do not show every alternative
- Keep code comments minimal and functional (what, not why-it-exists)
- Target: 50-100 lines of code with brief surrounding explanation
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