Frequently Asked Questions

Practical guidance for responsible and effective use of FormulationMM, especially for users without a computational modeling background.

FAQ

Frequently asked questions about FormulationMM.

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General and Responsible Use
Inputs and Parameterization
Modules and Methods
Output and Analysis

  • Is: an integrated platform that automates setup, execution, and analysis of physics-based modeling for drug formulations. See Protocols.
  • Is not: a substitute for experimental validation or expert judgment. Outputs are decision aids.
  • For quantitative claims: use advanced sampling such as umbrella sampling or well tempered metadynamics with uncertainty reporting.

  • Basics of molecular structure, protonation state, and net charge. Tutorials: AmberTools.
  • Ability to identify input issues such as overlapping atoms or NaN coordinates.
  • Concepts of force fields, charge models, sampling uncertainty, and validation against benchmarks.
  • Ability to read MD method sections and compare with reference cases.

  • Prefer 3D formats sdf, mol2, pdb over SMILES. Convert with Open Babel.
  • Verify protonation and charge for the target pH. Tools: PDB2PQR.
  • Ensure unique atom names, reasonable geometry, and no NaN coordinates.
  • Metal centers or exotic groups may need custom parameters and QM-derived charges. Guide: RESP.

  • CHARMM36 when protein or literature benchmarks are in CHARMM. Resource: CHARMM-GUI.
  • AMBER Lipid17 when protein or small molecules use Amber stacks. Resource: AmberMD.
  • Avoid mixing families unless cross-validation is performed and justified.

  • Up to 175 atoms: GAFF2 with RESP or AM1 BCC charges. Tool: Antechamber.
  • 175 atoms or more: GAFF2 with AM1 BCC for efficiency and consider QM charges for highly polar molecules.
  • For metal complexes or covalent inhibitors use tailored parameterization and expert review.

  • Use mainly for relative ranking within congeneric series.
  • Report standard deviations or confidence intervals. Tutorial: AMBER MMPBSA.
  • Expect systematic offsets in absolute values. Perform calibration to experimental trends.
  • Include entropy for flexible ligands. Methods: quasi harmonic and Schlitter.

Note: Scores indicate relative stability for screening and not absolute free energies.
  • Use 3D inputs and pre optimize ligand geometry. Converter: Open Babel.
  • Validate top poses by short AA MD and re rank with MMPB or SA including entropy.
  • For rigorous free energy compute PMF with umbrella sampling or use metadynamics. Toolkit: PLUMED.
  • For custom derivatives provide host charges and topology if available.

Note: A single trajectory is not equal to permeability. Use PMF for quantitative results.
  • Use umbrella sampling or metadynamics along a z distance or COM reaction coordinate. Reference: PMF in drug membrane permeation.
  • Match lipid composition, temperature, and ionic strength to experiments.
  • Provide convergence checks, window overlap, and error bars.

  • Choose collective variables carefully. Examples: z distance or COM projection for translocation, contact number or distance for binding and unbinding.
  • For metadynamics check bias deposition and avoid over biasing. Docs: PLUMED.
  • For umbrella sampling ensure window overlap and adequate sampling. Docs: GROMACS manual.
  • Include convergence diagnostics and independent repeats for robustness.

  • Use for qualitative miscibility and interaction ranking between drugs and polymers.
  • Not an absolute predictor of solubility or crystallization tendency. Combine with experiments such as DSC and PXRD.
  • Match polymer libraries and temperature windows to the intended application.

  • Fix random seeds. Record force field versions, mdp parameters, and software versions.
  • Archive inputs and scripts. Cite platform version and data access date.
  • Useful reading: Best practices in MD reproducibility.

  • Systems with metal coordination, reactive or covalent chemistry, or complex cyclodextrin derivatives.
  • Projects requiring quantitative free energy with defensible uncertainty or for regulatory decisions.
  • Contact our team: um.formulationmm@gmail.com. Also see FAQ and Safe Use and Protocols.

  • Each run exports a manifest with force field versions, key mdp parameters, random seeds, and timestamps. Keep this with your report for auditability.
  • Analysis scripts are grouped by observable names. Examples: density.py, densmap.py, h_bonds.py, contact.py, clustsize.py, interaction_energy.py, enthalpy.py, entropy.py, mmpbsa.py, potential.py.
  • Figures and tables are prefixed by module and observable. Example: tm_pmf_window_overlap.png, cd_mmpbsa_summary.csv.
  • Recommended reading for file conventions and metadata capture: MD reproducibility guidance.

  • density.py computes spatial number or mass density over time windows. Use consistent bin size and apply PBC correction. Reference: GROMACS manual.
  • densmap.py renders 2D or 3D heatmaps to locate high density drug regions in polymers or membranes. For solid dispersions, persistent hot spots indicate risk of phase separation.
  • Report: bin size, smoothing kernel, time window length, and confidence intervals from block averaging.
  • Pitfall: short windows may overfit transient fluctuations. Verify that features persist across independent blocks.

  • h_bonds.py counts hydrogen bonds using a donor acceptor distance cutoff around 3.5 Å and an angle criterion around 30 degrees. See gmx hbond.
  • contact.py evaluates heavy atom contacts at a chosen cutoff such as 4.0 Å and can output lifetimes by residence time analysis.
  • Report: mean and distribution of counts, lifetimes, and which functional groups dominate.
  • Pitfall: cutoff dependence. Provide sensitivity analysis by varying distance ±0.5 Å.

  • clustsize.py identifies connected components based on a contact graph and outputs size distributions and largest cluster fraction.
  • Interpretation: growth of the tail or sustained increase in the largest cluster fraction suggests aggregation tendencies.
  • Report: definition of contact, graph connectivity rule, and temporal averaging window.
  • Pitfall: finite size effects. Verify that conclusions hold when box size changes or under replicate runs.

  • interaction_energy.py decomposes nonbonded energies between components such as drug and polymer or drug and host.
  • enthalpy.py reports averaged enthalpic components and standard errors. entropy.py estimates configurational entropy via quasi harmonic or Schlitter approaches. Background: Schlitter method.
  • Interpretation: favorable enthalpy with unfavorable entropy indicates tight specific interactions; the reverse suggests hydrophobic or release driven tendencies.
  • Pitfall: entropy estimates are sensitive to sampling and filtering. Use long windows, remove global translations and rotations, and report uncertainty.

  • mmpbsa.py exports a table with ΔE_vdw, ΔE_ele, ΔG_solv, and optional −TΔS terms when entropy is included.
  • Report: number of frames, sampling stride, dielectric models, salt concentration, and whether internal dielectric scaling was used. Tutorial: AMBER MMPBSA.
  • Best practice: emphasize relative ranking and provide confidence intervals or bootstrap errors. Calibrate absolute magnitudes against experimental references when available.
  • Pitfall: mixing force field families or missing counter ions can bias polar terms. Cross check with interaction_energy.py trends.

  • potential.py plots total energy, temperature, and pressure trends for sanity checks. Manual: GROMACS.
  • Interpretation: stable temperature and pressure around set points and absence of long term drift in total energy under NVE or expected barostat behavior under NPT indicate healthy runs.
  • Pitfall: hidden constraints or neighbor list artifacts may cause spikes. Re run the short segment with tighter constraints or shorter time step to confirm.
Responsible Use: FormulationMM lowers the entry barrier for molecular modeling, but results are decision aids and not definitive answers. Interpretation needs scientific judgment and when appropriate consultation with experienced modelers. For quantitative claims such as permeability or binding free energy, use enhanced sampling and report uncertainty.