211 Property 9e66dfc0

Rowan Workflow Types Reference

Table of Contents

1. Property Prediction Workflows
2. Molecular Modeling Workflows
3. Protein-Ligand Workflows
4. Spectroscopy Workflows
5. Advanced Workflows

Property Prediction Workflows

pKa Calculation

Predict acid dissociation constants.

workflow = rowan.submit_pka_workflow(
    initial_molecule=mol,
    name="pKa calculation"
)

Output:

• strongest_acid: pKa of most acidic proton
• strongest_base: pKa of most basic site
• microscopic_pkas: List of site-specific pKa values
• tautomer_populations: Relative populations at pH 7

Redox Potential

Calculate oxidation/reduction potentials.

workflow = rowan.submit_redox_potential_workflow(
    initial_molecule=mol,
    name="redox potential"
)

Output:

• oxidation_potential: E° for oxidation (V vs SHE)
• reduction_potential: E° for reduction (V vs SHE)

Solubility Prediction

Predict aqueous and nonaqueous solubility.

workflow = rowan.submit_solubility_workflow(
    initial_molecule=mol,
    name="solubility"
)

Output:

• aqueous_solubility: Log S in water
• solubility_class: "High", "Medium", or "Low"

Hydrogen-Bond Basicity

Calculate H-bond acceptor strength.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="hydrogen_bond_basicity",
    workflow_data={},
    name="H-bond basicity"
)

Output:

• hb_basicity: pKBHX value

Bond Dissociation Energy (BDE)

Calculate homolytic bond dissociation energies.

workflow = rowan.submit_bde_workflow(
    initial_molecule=mol,
    bond_indices=(0, 1),  # Atom indices of bond
    name="BDE calculation"
)

Output:

• bde: Bond dissociation energy (kcal/mol)
• radical_stability: Stability of resulting radicals

Fukui Indices

Calculate reactivity indices for nucleophilic/electrophilic attack.

workflow = rowan.submit_fukui_workflow(
    initial_molecule=mol,
    name="Fukui indices"
)

Output:

• fukui_plus: Electrophilic attack susceptibility per atom
• fukui_minus: Nucleophilic attack susceptibility per atom
• fukui_dual: Dual descriptor per atom

Spin States

Calculate relative energies of different spin multiplicities.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="spin_states",
    workflow_data={},
    name="spin states"
)

Output:

• spin_state_energies: Energy of each multiplicity
• ground_state: Lowest energy multiplicity

ADME-Tox Predictions

Predict absorption, distribution, metabolism, excretion, and toxicity.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="admet",
    workflow_data={},
    name="ADMET"
)

Output:

• Various ADMET descriptors including:
  • logP, logD
  • herg_inhibition
  • cyp_inhibition
  • bioavailability
  • bbb_permeability

Molecular Modeling Workflows

Single-Point Energy

Calculate energy at fixed geometry.

workflow = rowan.submit_basic_calculation_workflow(
    initial_molecule=mol,
    workflow_type="single_point",
    name="single point"
)

Output:

• energy: Total energy (Hartree)
• dipole: Dipole moment vector
• mulliken_charges: Atomic partial charges

Geometry Optimization

Optimize molecular geometry to minimum energy.

workflow = rowan.submit_basic_calculation_workflow(
    initial_molecule=mol,
    workflow_type="optimization",
    name="optimization"
)

Output:

• final_molecule: Optimized structure
• energy: Final energy (Hartree)
• convergence: Optimization details

Vibrational Frequencies

Calculate IR/Raman frequencies and thermochemistry.

workflow = rowan.submit_basic_calculation_workflow(
    initial_molecule=mol,
    workflow_type="frequency",
    name="frequency"
)

Output:

• frequencies: Vibrational frequencies (cm⁻¹)
• ir_intensities: IR intensities
• zpe: Zero-point energy
• thermal_corrections: Enthalpy, entropy, Gibbs free energy
• imaginary_frequencies: Count of negative frequencies

Conformer Search

Generate and optimize conformer ensemble.

workflow = rowan.submit_conformer_search_workflow(
    initial_molecule=mol,
    name="conformer search"
)

Output:

• conformers: List of conformer structures with energies
• lowest_energy_conformer: Global minimum structure
• boltzmann_weights: Population weights at 298 K

Tautomer Search

Enumerate and rank tautomers.

workflow = rowan.submit_tautomer_search_workflow(
    initial_molecule=mol,
    name="tautomer search"
)

Output:

• tautomers: List of tautomer structures
• energies: Relative energies
• populations: Boltzmann populations

Dihedral Scan

Scan torsion angle energy surface.

workflow = rowan.submit_dihedral_scan_workflow(
    initial_molecule=mol,
    dihedral_indices=(0, 1, 2, 3),  # Atom indices
    name="dihedral scan"
)

Output:

• angles: Dihedral angles scanned (degrees)
• energies: Energy at each angle
• barrier_height: Rotation barrier (kcal/mol)

Multistage Optimization

Progressive refinement with multiple methods.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="multistage_optimization",
    workflow_data={
        "stages": ["gfn2_xtb", "aimnet2", "dft"]
    },
    name="multistage opt"
)

Output:

• final_molecule: Optimized structure
• stage_energies: Energy after each stage

Transition State Search

Find transition state geometry.

workflow = rowan.submit_ts_search_workflow(
    initial_molecule=mol,  # Starting guess near TS
    name="TS search"
)

Output:

• ts_structure: Transition state geometry
• imaginary_frequency: Single imaginary frequency
• barrier_height: Activation energy

Strain Calculation

Calculate ligand strain energy.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="strain",
    workflow_data={},
    name="strain"
)

Output:

• strain_energy: Conformational strain (kcal/mol)
• reference_energy: Lowest energy conformer energy

Orbital Calculation

Calculate molecular orbitals.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="orbitals",
    workflow_data={},
    name="orbitals"
)

Output:

• homo_energy: HOMO energy (eV)
• lumo_energy: LUMO energy (eV)
• homo_lumo_gap: Band gap (eV)
• orbital_coefficients: MO coefficients

Protein-Ligand Workflows

Docking

Dock ligand to protein binding site.

workflow = rowan.submit_docking_workflow(
    protein=protein_uuid,
    pocket={
        "center": [10.0, 20.0, 30.0],
        "size": [20.0, 20.0, 20.0]
    },
    initial_molecule=mol,
    executable="vina",           # "vina" or "qvina2"
    scoring_function="vinardo",  # "vina" or "vinardo"
    exhaustiveness=8,
    do_csearch=True,             # Conformer search before docking
    do_optimization=True,        # Optimize conformers
    do_pose_refinement=True,     # Refine poses with QM
    name="docking"
)

Output:

• docking_score: Best Vina score (kcal/mol)
• poses: List of docked poses with scores
• ligand_strain: Strain energy of bound conformer
• pose_sdf: SDF file of poses

Batch Docking

Screen multiple ligands against one target.

workflow = rowan.submit_batch_docking_workflow(
    protein=protein_uuid,
    pocket=pocket_dict,
    smiles_list=["CCO", "c1ccccc1", "CC(=O)O"],
    executable="qvina2",
    scoring_function="vina",
    name="batch docking"
)

Output:

• results: List of docking results per ligand
• rankings: Sorted by score

Protein Cofolding

Predict protein-ligand complex structure using AI.

workflow = rowan.submit_protein_cofolding_workflow(
    initial_protein_sequences=["MSKGEELFT..."],
    initial_smiles_list=["CCO"],
    model="boltz_2",       # "boltz_1x", "boltz_2", "chai_1r"
    use_msa_server=False,  # Use MSA for better accuracy
    use_potentials=True,   # Apply physical constraints
    compute_strain=False,  # Calculate ligand strain
    do_pose_refinement=False,
    name="cofolding"
)

Models:

• chai_1r: Chai-1 model (~2 min)
• boltz_1x: Boltz-1 model (~2 min)
• boltz_2: Boltz-2 model (latest, recommended)

Output:

• structure_pdb: Predicted complex structure
• ptm_score: Predicted TM score (0-1, higher = more confident)
• interface_ptm: Interface prediction confidence
• aggregate_score: Combined confidence metric
• ligand_rmsd: If reference available

Pose-Analysis MD

Molecular dynamics simulation of docked pose.

workflow = rowan.submit_workflow(
    initial_molecule=mol,
    workflow_type="pose_analysis_md",
    workflow_data={
        "protein_uuid": protein_uuid,
        "pose_sdf": pose_sdf_content
    },
    name="pose MD"
)

Output:

• trajectory: MD trajectory file
• rmsd_over_time: Ligand RMSD
• interactions: Protein-ligand interactions

Spectroscopy Workflows

NMR Prediction

Predict NMR chemical shifts.

workflow = rowan.submit_nmr_workflow(
    initial_molecule=mol,
    name="NMR"
)

Output:

• h_shifts: ¹H chemical shifts (ppm)
• c_shifts: ¹³C chemical shifts (ppm)
• coupling_constants: J-coupling values

Ion Mobility

Predict collision cross-section for mass spectrometry.

workflow = rowan.submit_ion_mobility_workflow(
    initial_molecule=mol,
    name="ion mobility"
)

Output:

• ccs: Collision cross-section (Ų)
• conformer_ccs: CCS per conformer

Advanced Workflows

Molecular Descriptors

Calculate comprehensive descriptor set.

workflow = rowan.submit_descriptors_workflow(
    initial_molecule=mol,
    name="descriptors"
)

Output:

• 2D descriptors (RDKit-based)
• 3D descriptors (xTB-based)
• Electronic descriptors

MSA (Multiple Sequence Alignment)

Generate MSA for protein sequences.

workflow = rowan.submit_msa_workflow(
    sequences=["MSKGEELFT..."],
    name="MSA"
)

Output:

• msa: Multiple sequence alignment
• coverage: Sequence coverage

Protein Binder Design (BoltzGen)

Design protein binders.

workflow = rowan.submit_workflow(
    workflow_type="protein_binder_design",
    workflow_data={
        "target_sequence": "MSKGEELFT...",
        "target_hotspots": [10, 15, 20]
    },
    name="binder design"
)

Output:

• designed_sequences: Binder sequences
• confidence_scores: Per-design confidence

Workflow Parameters Reference

Common Parameters

All workflow submission functions accept:

Method Selection

For basic calculations, specify method:

workflow = rowan.submit_basic_calculation_workflow(
    initial_molecule=mol,
    workflow_type="optimization",
    workflow_data={
        "method": "gfn2_xtb",  # or "aimnet2", "dft"
        "basis_set": "def2-SVP"  # for DFT
    }
)

Available Methods:

• Neural network: aimnet2, egret
• Semiempirical: gfn1_xtb, gfn2_xtb
• DFT: b3lyp, pbe, wb97x