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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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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
  1. Distinctive:

    Right: Use PROPKA or H++ for acidic/basic pockets

    line 78
  2. Very specific:

    Wrong: Default exhaustiveness = 8 for large/flexible ligands

    line 85
  3. Distinctive:

    Right: Use 32+ for production; 64+ for >8 rotatable bonds

    line 86
  4. Very specific:

    Right: Use prepare_receptor (ADFR) or meeko for production

    line 94
  5. Distinctive:

    Right: Only compare within same receptor + grid.

    line 102
  6. Distinctive:

    Keep code comments minimal and functional (what, not why-it-exists)

    line 110

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

ParameterRecommendedWhen to change
exhaustiveness3264+ for >8 rotatable bonds
num_modes10Increase for ensemble analysis
energy_range3 kcal/molWiden to capture diverse poses
Box padding+10–15 ÅLarger for allosteric sites
Protonation pH7.0 (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 = 8 for 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 -xr PDBQT for production virtual screens Right: Use prepare_receptor (ADFR) or meeko for production Why: obabel may miss altlocs, metals, or non-standard residues
  • Wrong: Running obabel --gen3d on 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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