wf = rowan.submit_descriptors_workflow(
    "CC(=O)Oc1ccccc1C(=O)O",  # positional arg, accepts SMILES string
    name="aspirin descriptors",
)

result = wf.result()
print(result.descriptors['MW'])    # 180.16
print(result.descriptors['SLogP']) # 1.19
print(result.descriptors['TPSA'])  # 59.44
print(result.data['descriptors'])
# {'MW': 180.16, 'SLogP': 1.19, 'TPSA': 59.44, 'nHBDon': 1.0, 'nHBAcc': 4.0, ...}

# Fast path: SMILES input with full acid+base coverage (use starling method when available)
wf = rowan.submit_pka_workflow(
    initial_molecule="c1ccccc1O",       # phenol SMILES; param is initial_molecule, not initial_smiles
    method="starling",   # fast SMILES method, covers acid+base; chemprop_nevolianis2025 is deprotonation-only
    name="phenol pKa",
)

result = wf.result()
print(result.strongest_acid)    # 9.81 (pKa of the most acidic site)
print(result.conjugate_bases)   # list of {pka, smiles, atom_index, ...} per deprotonatable site

wf = rowan.submit_macropka_workflow(
    initial_smiles="CN1CCN(CC1)C2=NC=NC3=CC=CC=C32",  # imidazole
    min_pH=0,
    max_pH=14,
    min_charge=-2,  # default
    max_charge=2,   # default
    compute_aqueous_solubility=True,  # default
    name="imidazole macropKa",
)

result = wf.result()
print(result.pka_values)               # list of pKa values
print(result.logd_by_ph)               # dict of {pH: logD}
print(result.aqueous_solubility_by_ph) # dict of {pH: solubility}
print(result.isoelectric_point)        # isoelectric point
print(result.data)
# {'pKa_values': [...], 'logD_by_pH': {...}, 'aqueous_solubility_by_pH': {...}, ...}

wf = rowan.submit_conformer_search_workflow(
    initial_molecule="CCOC(=O)N1CCC(CC1)Oc1ncnc2ccccc12",
    num_conformers=50,  # Optional: override default
    name="conformer search",
)

result = wf.result()
print(result.conformer_energies)  # [0.0, 1.2, 2.5, ...]
print(result.conformer_molecules)  # List of 3D molecules
print(result.best_conformer)  # Lowest-energy conformer

wf = rowan.submit_tautomer_search_workflow(
    initial_molecule="O=c1[nH]ccnc1",  # or keto tautomer
    name="imidazolone tautomers",
)

result = wf.result()
print(result.best_tautomer)  # Most stable SMILES string
print(result.tautomers)      # List of tautomeric SMILES
print(result.molecules)      # List of molecule objects

# Upload protein once, reuse in multiple workflows
protein = rowan.upload_protein(
    name="CDK2",
    file_path="cdk2.pdb",
)

# Define binding pocket
pocket = {
    "center": [10.5, 24.2, 31.8],
    "size": [18.0, 18.0, 18.0],
}

# Submit docking
wf = rowan.submit_docking_workflow(
    protein=protein,
    pocket=pocket,
    initial_molecule="CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",
    do_pose_refinement=True,
    do_conformer_search=True,
    name="lead docking",
)

result = wf.result()
print(result.scores)  # Docking scores (kcal/mol)
print(result.best_pose)  # Mol object with 3D coordinates
print(result.data)  # Raw result dict

# Analogue series (e.g., SAR campaign)
analogues = [
    "CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",    # reference
    "CCNc1ncc(c(Nc2ccc(Cl)cc2)n1)-c1cccnc1",   # chloro
    "CCNc1ncc(c(Nc2ccc(OC)cc2)n1)-c1cccnc1",   # methoxy
    "CCNc1ncc(c(Nc2cc(C)c(F)cc2)n1)-c1cccnc1", # methyl, fluoro
]

wf = rowan.submit_analogue_docking_workflow(
    analogues=analogues,
    initial_molecule=analogues[0],  # Reference ligand
    protein=protein,
    pocket=pocket,
    name="SAR series docking",
)

result = wf.result()
print(result.analogue_scores)  # List of scores for each analogue
print(result.best_poses)  # List of poses

wf = rowan.submit_msa_workflow(
    initial_protein_sequences=[
        "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVP"
    ],
    output_formats=["colabfold", "chai", "boltz"],
    name="target MSA",
)

result = wf.result()
result.download_files()  # Downloads alignments to disk

wf = rowan.submit_protein_cofolding_workflow(
    initial_protein_sequences=[
        "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVP"
    ],
    initial_smiles_list=[
        "CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1"
    ],
    name="protein-ligand cofolding",
)

result = wf.result()
print(result.predictions)  # List of predicted structures
print(result.messages)  # Model metadata/warnings

predicted_structure = result.get_predicted_structure()
predicted_structure.write("predicted_complex.pdb")