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Drug Complex System Builder

skill-learningmatter-mit-atomisticskills-drug-complex-system-builder · by learningmatter-mit

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Install

$ agentstack add skill-learningmatter-mit-atomisticskills-drug-complex-system-builder

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Security review

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No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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About

drug-complex-system-builder

Goal

To take a prepared protein (PDB) and a validated ligand pose (SDF) and produce a fully parameterized, solvated, ion-neutralized OpenMM simulation bundle ready for [drug-protein-ligand-md](../drug-protein-ligand-md/SKILL.md).

The output bundle includes:

  • Serialized OpenMM System XML (force field parameters, constraints)
  • Full-precision initial state XML (positions + box vectors for exact restart)
  • Solvated PDB with protein + ligand + water + ions (for visualization)
  • Provenance JSON recording all build parameters

Instructions

1. Prepare inputs

Required inputs:

  • Receptor PDB: from [drug-protein-prep](../drug-protein-prep/SKILL.md) (protonated, missing residues resolved).
  • Ligand SDF: from [drug-pose-validation](../drug-pose-validation/SKILL.md) or [drug-docking-vina](../drug-docking-vina/SKILL.md). Must have 3D coordinates in the receptor frame and explicit hydrogens.

2. Build the solvated complex

# Env: drugmd-agent
python .agents/skills/drug-complex-system-builder/scripts/build_complex.py \
  --receptor docking/inputs/protein_prepared.pdb \
  --ligand docking/validation/valid_poses.sdf \
  --ligand_ff openff-2.2.0 \
  --protein_ff amber/ff14SB \
  --water_model tip3p \
  --box_padding 12.0 \
  --ionic_strength 0.15 \
  --output_dir md/system/

Key parameters:

  • --ligand_ff: Force field for the ligand. Options: openff-2.2.0 (Sage, recommended), gaff-2.11. OpenFF Sage is generally preferred for drug-like molecules.
  • --protein_ff: Protein force field. Default: amber/ff14SB.
  • --water_model: Water model. Default: tip3p. Options: tip3p, tip3pfb, tip4pew, opc, spce. Use tip3pfb or opc for better accuracy at higher cost.
  • --box_padding: Minimum distance from solute to box edge in Angstroms (default: 12.0). Use 10-12 A for production; smaller values risk periodic image artifacts.
  • --ionic_strength: Target NaCl concentration in mol/L (default: 0.15, physiological). The system is always charge-neutralized first; additional ion pairs are added to reach the target ionic strength. The ionic strength calculation does not count the neutralization ions (they are treated as bound to the solute).
  • --pose_index: Which pose from the SDF to use (default: 0, the top-ranked pose).
  • --box_shape: Simulation box geometry (default: cube). Options: cube, dodecahedron, octahedron. Dodecahedron and octahedron use ~30% less water for the same minimum solute-edge distance.
  • --hydrogen_mass: Hydrogen mass in amu for hydrogen mass repartitioning (default: 4.0). With HMR (3-4 amu), the script uses AllBonds constraints, enabling 4-5 fs timesteps (OpenMM recommends 5 fs with LangevinMiddleIntegrator). Set to 1.008 to disable HMR (uses HBonds constraints, requires 2 fs timestep). Note: at 4 amu, methyl carbons become lighter than their bonded hydrogens, which can affect dynamics in some systems (particularly membranes). Use 3 amu if this is a concern. The downstream MD skill must use a matching timestep (check hmr_enabled and constraints in the provenance JSON).

3. Inspect outputs

The script produces:

  • md/system/complex_solvated.pdb: solvated system for visualization (PDB precision: 0.001 A)
  • md/system/system.xml: serialized OpenMM System (force field parameters, constraints)
  • md/system/state_initial.xml: full-precision positions and box vectors for simulation restart
  • md/system/build_provenance.json: records all build parameters, atom counts, box dimensions, HMR status, constraint type

Visually inspect complex_solvated.pdb to verify:

  • The ligand is in the expected binding pocket
  • No steric clashes between protein and ligand
  • Water fills the box uniformly
  • Ions are distributed (not clustered)

4. Troubleshooting

Common issues:

  • Ligand parameterization fails: ensure the ligand SDF has explicit hydrogens and correct bond orders. Re-run [drug-ligand-prep](../drug-ligand-prep/SKILL.md) if needed. The script assigns AM1-BCC partial charges automatically; any pre-existing charges in the SDF are overwritten to ensure deterministic behavior.
  • Steric clash warning: the script checks minimum protein-ligand interatomic distances before solvation. If you see a clash warning, the docking pose may need refinement. Mild clashes (1.0-1.5 A) can often be resolved by energy minimization, but severe clashes (100 heavy atoms) or metal-containing compounds, parameterization may require manual intervention.
  • Protein force field: Only Amber-family force fields (ff14SB, ff19SB) are supported through openmmforcefields. CHARMM support would require a different builder.
  • Box shape: Defaults to cubic. Dodecahedron and truncated octahedron are supported via --box_shape (requires OpenMM 8.0+).

References

  • Maier, J. A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K. E.; Simmerling, C. ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696-3713. https://doi.org/10.1021/acs.jctc.5b00255
  • Boothroyd, S.; Behara, P. K.; Madin, O. C.; et al. Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field. J. Chem. Theory Comput. 2023, 19, 3251-3275. https://doi.org/10.1021/acs.jctc.3c00039
  • Eastman, P.; Swails, J.; Chodera, J. D.; et al. OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics. PLoS Comput. Biol. 2017, 13, e1005659. https://doi.org/10.1371/journal.pcbi.1005659

Author: Matthew Cox Contact: GitHub @mcox3406

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.