Mopac

MOPAC Semi-Empirical Quantum Chemistry

Overview

MOPAC provides semi-empirical quantum chemistry calculations that are ~1000x faster than DFT while maintaining reasonable accuracy for many applications. This skill enables rapid computational investigation of reaction mechanisms, transition states, activation barriers, and molecular properties. MOPAC is ideal for high-throughput screening and rapid hypothesis testing in drug discovery and materials chemistry.

Core Capabilities

1. Geometry Optimization

Molecular Structures:

Optimize small molecules and drug-like compounds:

# PM6 method (balanced speed/accuracy)
# PM7 method (improved accuracy, slightly slower)
# PM6-D3H4X (includes dispersion corrections for D3 interactions)

mopac_optimized = optimize_structure(
    smiles="CCO",
    method="PM6",
    convergence="tight"
)

# Get optimized geometry and energy
energy = mopac_optimized.get_energy()
structure = mopac_optimized.get_structure()

Key Features:

• Fast convergence (seconds to minutes)
• Suitable for molecules up to ~100 atoms efficiently
• Can handle aromatic, heterocyclic, organometallic systems
• Includes hydrogen bonding, van der Waals interactions

2. Transition State Finding

Activation Barriers:

Locate and characterize transition states:

# Min-TS-Min pathway
# 1. Optimize reactant
# 2. Find transition state (TS keyword in MOPAC)
# 3. Optimize product

ts_structure = find_transition_state(
    reactant="reactant.smi",
    product="product.smi",
    method="PM6"
)

activation_barrier = ts_structure.get_energy() - reactant.get_energy()

Applications:

• Drug metabolism prediction (phase I/II mechanisms)
• Chemical reactivity assessment
• Reaction selectivity prediction
• Mechanistic hypothesis validation

3. Molecular Properties

Calculate from Quantum Wavefunction:

- Dipole moment
- Polarizability
- Electronegativity
- Electrostatic potential (ESP)
- Partial charges (Mulliken, Löwdin)
- Orbital energies (HOMO, LUMO, gap)
- Hardness, softness (chemical potential)
- Reactivity indices (Fukui functions)

For Drug Design:

• Predict pKa (using COSMO solvation)
• Estimate metabolic susceptibility
• Assess chemical stability
• Identify reactive hot spots

4. Solvation Effects

COSMO Implicit Solvent Model:

Calculate properties in aqueous/organic media:

# COSMO (Conductor-like Screening Model)
# Implicit solvent descriptions for:
# - Water
# - DMSO, DMF
# - Chloroform, dichloromethane
# - Alcohols

aqueous_energy = calculate_solvation(
    structure="molecule.xyz",
    solvent="water",
    method="PM6"
)

# pKa prediction from desolvation energy

5. Vibrational Analysis

Frequencies and Thermochemistry:

Compute IR-active vibrational modes:

- Vibrational frequencies
- Infrared intensities
- Raman scattering
- Zero-point energy (ZPE)
- Enthalpy corrections
- Entropy corrections
- Gibbs free energy at any temperature

MOPAC Methods

PM6 (Parametrized Model 6)

• Accuracy: Good for organics, heteroatoms, hydrogen bonding
• Speed: Very fast (~seconds)
• Use: General purpose, drug discovery, screening

PM7

• Accuracy: Improved over PM6 (metallorganic, transition metals)
• Speed: Slightly slower than PM6 (~10-30 seconds)
• Use: More accurate properties, metal-containing systems

PM6-D3H4X

• Accuracy: Best for weak interactions (dispersion, H-bonding)
• Speed: ~2x PM6
• Use: Noncovalent interactions, supramolecular chemistry, protein-ligand docking

Use Cases

Drug Discovery:

• ADMET property prediction
• Metabolite structure elucidation
• pKa/logP calculations
• Off-target toxicity prediction (reactivity-based)

Reaction Mechanism:

• Identify rate-limiting step
• Predict regioselectivity
• Understand stereoselectivity
• Design synthetic routes

Materials Chemistry:

• Conjugation in organic semiconductors
• Band gap prediction for dyes
• Hydrogen storage materials
• Supramolecular assembly design

Chemical Stability:

• Hydrolysis rates
• Oxidative degradation pathways
• Shelf-life prediction
• Storage condition optimization

Integration with Other Skills

Input:

• SMILES from pubchem (convert to structures)
• Protein residues from uniprot (study interaction mechanisms)
• Known ligands from chembl (understand binding mechanisms)

Output:

• Transition states for mechanistic understanding
• Properties for ML model training
• Reactivity predictions for chemical screening
• Activation barriers for kinetic modeling

Accuracy vs. DFT

When to use MOPAC:

• ✅ Fast screening (drug discovery)
• ✅ Reaction mechanism validation
• ✅ Property prediction (pKa, dipole)
• ✅ Large molecule ensembles

When to use DFT:

• ✅ High accuracy needed
• ✅ Weak interactions (dispersion)
• ✅ Excited state chemistry
• ✅ Periodic systems (crystals)

Limitations

• Parameterized for specific atom types (main group + some transition metals)
• Less accurate for charged species or transition metals
• Cannot predict NMR shifts or electron density maps
• No excited states (use DFT)
• Parameterization based on training set (may have extrapolation errors)

Example Workflow

# 1. Optimize structure
python mopac_optimize.py --smiles "CC(C)CC(N)C(=O)O" --method PM6

# 2. Calculate properties
python mopac_properties.py --structure optimized.xyz --include-frequencies

# 3. Find transition state
python mopac_transition_state.py --reactant reactant.xyz --product product.xyz

# 4. Predict pKa
python mopac_pka.py --structure molecule.xyz --solvent water

# 5. Analyze reactivity
python mopac_reactivity.py --structure molecule.xyz --method PM6-D3H4X

References

• MOPAC Manual: http://openmopac.net/
• MOPAC2016 Tutorial: http://openmopac.net/MOPAC2016_Tutorial.pdf
• PM6 Paper: Stewart, J.J.P. (2007) JMC
• PM7 Paper: Stewart, J.J.P. (2013) JMC