Q2MM

Quantum-guided molecular mechanics force field optimization.

Q2MM optimizes molecular mechanics (MM) force field parameters by minimizing the difference between MM-calculated properties and quantum mechanics (QM) reference data. It supports multiple QM and MM backends through a plugin architecture.

Quick Start

pip install -e .

from q2mm.io import GaussLog, Mol2
from q2mm.forcefields import MM3

# Parse QM reference data
log = GaussLog("ethane.log")
hessian = log.structures[0].hess

# Parse molecular structure
mol2 = Mol2("ethane.mol2")
print(f"Atoms: {len(mol2.structures[0].atoms)}")

# Load force field
ff = MM3("mm3.fld")
ff.import_ff()
print(f"Parameters: {len(ff.params)}")

Installation

Requirements: Python 3.9+

pip install -e .          # Basic
pip install -e ".[dev]"   # With pytest + ruff
pip install -e ".[amber]" # With parmed (AMBER support)

QM/MM Backends

Backend	Type	License	Install
Psi4	QM	BSD-3 (open source)	conda install psi4 -c conda-forge
Tinker	MM	Free (academic)	download
Gaussian	QM	Commercial	Site license
Schrodinger	QM/MM	Commercial	Site license

Package Structure

q2mm/
├── core/          # Optimization engine (gradient, simplex, objective function)
├── backends/      # QM/MM engine integrations (Psi4, Tinker, etc.)
├── io/            # File format parsers (Gaussian, Jaguar, MOL2, MAE)
├── forcefields/   # Force field types (MM3, AMBER, Tinker)
├── cli/           # Command-line interface
└── seminario.py   # QFUERZA/Seminario force constant estimation

examples/          # Example workflows and training data
data/              # Reference structures (ligands, substrates, reactions)
scripts/           # Utility scripts and screening tools

Development

pip install -e ".[dev]"
python -m pytest -v
ruff check q2mm/ test/ scripts/

Citations

If you use Q2MM in your research, please cite the relevant publications:

Core Method

• Norrby, P.-O. Selectivity in Asymmetric Synthesis from QM-Guided Molecular Mechanics. J. Mol. Struct. (THEOCHEM) 2000, 506, 9–16. DOI: 10.1016/S0166-1280(00)00398-5

• Hansen, E.; Rosales, A. R.; Tutkowski, B.; Norrby, P.-O.; Wiest, O. Prediction of Stereochemistry using Q2MM. Acc. Chem. Res. 2016, 49, 996–1005. DOI: 10.1021/acs.accounts.6b00037

• Rosales, A. R.; Quinn, T. R.; Wahlers, J.; Tomberg, A.; Zhang, X.; Helquist, P.; Wiest, O.; Norrby, P.-O. Application of Q2MM to Predictions in Stereoselective Synthesis. Chem. Commun. 2018, 54, 8294–8301. DOI: 10.1039/C8CC03695K

QFUERZA / Seminario Method

• Farrugia, L. M.; Helquist, P.; Norrby, P.-O.; Wiest, O. Rapid FF Generation via Hessian-Informed Initial Parameters and Automated Refinement. J. Chem. Theory Comput. 2026, 22, 469–476. DOI: 10.1021/acs.jctc.4c01372

Applications

• Rosales, A. R.; Wahlers, J.; Limé, E.; Meadows, R. E.; Leslie, K. W.; Savin, R.; Bell, F.; Hansen, E.; Helquist, P.; Munday, R. H.; Wiest, O.; Norrby, P.-O. Rapid Virtual Screening of Enantioselective Catalysts using CatVS. Nat. Catal. 2019, 2, 41–45. DOI: 10.1038/s41929-018-0193-3

• Burai Patrascu, M.; Pottel, J.; Pinus, S.; Bezanson, M.; Norrby, P.-O.; Moitessier, N. Virtual Chemist: Prediction of Enantioselectivity. Nat. Catal. 2020, 3, 574–584. DOI: 10.1038/s41929-020-0467-0

• Rosales, A. R.; Ross, S. P.; Helquist, P.; Norrby, P.-O.; Sigman, M. S.; Wiest, O. Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction. J. Am. Chem. Soc. 2020, 142, 9700–9707. DOI: 10.1021/jacs.0c01979

• Wahlers, J.; Maloney, M.; Salahi, F.; Rosales, A. R.; Helquist, P.; Norrby, P.-O.; Wiest, O. Stereoselectivity Predictions for the Pd-Catalyzed 1,4-Conjugate Addition. J. Org. Chem. 2021, 86, 5660–5667. DOI: 10.1021/acs.joc.0c02918

• Wahlers, J.; Margalef, J.; Hansen, E.; Bayesteh, A.; Helquist, P.; Diéguez, M.; Pàmies, O.; Wiest, O.; Norrby, P.-O. Proofreading Experimentally Assigned Stereochemistry through Q2MM Predictions. Nat. Commun. 2021, 12, 6508. DOI: 10.1038/s41467-021-27065-2

• Quinn, T. R.; Patel, H. N.; Koh, K. H.; Haines, B. E.; Norrby, P.-O.; Helquist, P.; Wiest, O. Automated Fitting of Transition State Force Fields for Biomolecular Simulations. PLOS ONE 2022, 17, e0264960. DOI: 10.1371/journal.pone.0264960

• Wahlers, J.; Rosales, A. R.; Berkel, N.; Forbes, A.; Helquist, P.; Norrby, P.-O.; Wiest, O. MM3* Force Field for Ferrocenyl Ligands. J. Org. Chem. 2022, 87, 12334–12341. DOI: 10.1021/acs.joc.2c01396

• Maloney, M. P.; Stenfors, B. A.; Helquist, P.; Norrby, P.-O.; Wiest, O. Interplay of Computation and Experiment in Enantioselective Catalysis. ACS Catal. 2023, 13, 14285–14299. DOI: 10.1021/acscatal.3c03706

License

BSD-3-Clause. See LICENSE.