Using QCArchive with the OpenFF Toolkit

Here we show how to create OpenFF molecules safely from data in the QCArchive using the CMILES entries. This transformation relies on the "canonical_isomeric_explicit_hydrogen_mapped_smiles".

First load up the client you wish to connect to, in this case, we use the public instance.

import qcportal

from openff.toolkit import Molecule

client = qcportal.PortalClient("https://api.qcarchive.molssi.org:443")

print(client.list_datasets_table())
/home/runner/micromamba/envs/openff-docs-examples/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).
  from pandas.core.computation.check import NUMEXPR_INSTALLED
  id  type                record_count  name
----  ----------------  --------------  ----------------------------------------------------------------------------------
  35  torsiondrive                 454  OpenFF Fragmenter Phenyl Benchmark
  36  torsiondrive                 820  OpenFF Group1 Torsions
  41  optimization                 937  OpenFF Optimization Set 1
  42  torsiondrive                  86  Fragment Stability Benchmark
  43  optimization                1132  SMIRNOFF Coverage Set 1
  45  optimization               25500  OpenFF VEHICLe Set 1
  48  torsiondrive                 585  SMIRNOFF Coverage Torsion Set 1
  49  optimization                 189  OpenFF NCI250K Boron 1
  50  optimization               19714  OpenFF Discrepancy Benchmark 1
  57  torsiondrive                 795  OpenFF Substituted Phenyl Set 1
  68  optimization                 352  Pfizer Discrepancy Optimization Dataset 1
  69  optimization                6670  FDA Optimization Dataset 1
  70  torsiondrive                 227  Pfizer Discrepancy Torsion Dataset 1
  71  gridoptimization             311  OpenFF Trivalent Nitrogen Set 1 (deprecated)
 148  reaction                     994  AlkIsod14
 149  reaction                    1846  BHPERI26
 151  singlepoint                  937  OpenFF Optimization Set 1
 152  singlepoint                48280  OpenFF VEHICLe Set 1
 153  singlepoint                  189  OpenFF NCI250K Boron 1
 156  reaction                     710  CYCONF
 157  manybody                    1946  DS14
 158  manybody                    1390  FmH2O10
 159  singlepoint                18864  OpenFF Discrepancy Benchmark 1
 160  manybody                    2919  NC15
 161  reaction                    4544  Butanediol65
 162  manybody                    2085  HB15
 163  reaction                    1065  ACONF
 164  reaction                    1278  HNBrBDE18
 165  reaction                   12936  MPCONF196
 167  reaction                     781  AlkIsomer11
 168  manybody                    2919  A21
 169  manybody                    3336  A24
 170  reaction                    2556  BSR36
 171  reaction                    2130  BH76RC
 172  manybody                    1668  AlkBind12
 173  reaction                     426  C20C24
 174  singlepoint                 1109  SMIRNOFF Coverage Set 1
 194  torsiondrive                  31  OpenFF Primary TorsionDrive Benchmark 1
 195  torsiondrive                 227  OpenFF Full TorsionDrive Benchmark 1
 196  optimization                1885  OpenFF Primary Optimization Benchmark 1
 197  optimization               26736  OpenFF Full Optimization Benchmark 1
 213  singlepoint                    0  Solvated Protein Fragments
 217  torsiondrive                   4  TorsionDrive Paper
 231  gridoptimization             311  OpenFF Trivalent Nitrogen Set 1
 232  optimization                6567  Kinase Inhibitors: WBO Distributions
 235  torsiondrive                 259  OpenFF Primary Benchmark 1 Torsion Set
 237  gridoptimization             311  OpenFF Trivalent Nitrogen Set 2
 239  gridoptimization             126  OpenFF Trivalent Nitrogen Set 3
 241  torsiondrive                 595  OpenFF Primary Benchmark 2 Torsion Set
 242  torsiondrive                  19  OpenFF Group1 Torsions 2
 243  torsiondrive                   6  OpenFF Group1 Torsions 3
 245  torsiondrive                  65  OpenFF Gen 2 Torsion Set 1 Roche
 246  torsiondrive                  93  OpenFF Gen 2 Torsion Set 2 Coverage
 247  torsiondrive                  38  OpenFF Gen 2 Torsion Set 3 Pfizer Discrepancy
 248  torsiondrive                 106  OpenFF Gen 2 Torsion Set 4 eMolecules Discrepancy
 249  torsiondrive                 100  OpenFF Gen 2 Torsion Set 5 Bayer
 250  torsiondrive                   7  OpenFF Gen 2 Torsion Set 6 Supplemental
 251  optimization                 298  OpenFF Gen 2 Opt Set 1 Roche
 253  optimization                 373  OpenFF Gen 2 Opt Set 2 Coverage
 254  optimization                2201  OpenFF Gen 2 Opt Set 4 eMolecules Discrepancy
 255  optimization                 197  OpenFF Gen 2 Opt Set 3 Pfizer Discrepancy
 256  torsiondrive                 142  OpenFF Gen 2 Torsion Set 1 Roche 2
 257  torsiondrive                 157  OpenFF Gen 2 Torsion Set 2 Coverage 2
 258  torsiondrive                  82  OpenFF Gen 2 Torsion Set 3 Pfizer Discrepancy 2
 259  torsiondrive                 272  OpenFF Gen 2 Torsion Set 4 eMolecules Discrepancy 2
 260  singlepoint                  298  OpenFF Gen 2 Opt Set 1 Roche
 262  singlepoint                  352  OpenFF Gen 2 Opt Set 2 Coverage
 263  singlepoint                  197  OpenFF Gen 2 Opt Set 3 Pfizer Discrepancy
 264  singlepoint                 2181  OpenFF Gen 2 Opt Set 4 eMolecules Discrepancy
 265  torsiondrive                 219  OpenFF Gen 2 Torsion Set 5 Bayer 2
 266  torsiondrive                  22  OpenFF Gen 2 Torsion Set 6 Supplemental 2
 268  torsiondrive                1459  SiliconTX Torsion Benchmark Set 1
 270  optimization                1850  OpenFF Gen 2 Opt Set 5 Bayer
 272  singlepoint                 1772  OpenFF Gen 2 Opt Set 5 Bayer
 275  torsiondrive                 462  Fragmenter paper
 278  torsiondrive                 440  OpenFF Fragmenter Validation 1.0
 279  optimization                3485  OpenFF Ehrman Informative Optimization v0.1
 281  optimization                3485  OpenFF Ehrman Informative Optimization v0.2
 282  torsiondrive                  20  OpenFF DANCE 1 eMolecules t142 v1.0
 283  torsiondrive                  87  OpenFF Rowley Biaryl v1.0
 284  optimization                 576  OpenFF Protein Fragments v1.0
 285  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ DZVP v1.0
 286  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ def2-TZVP v1.0
 287  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ def2-TZVPD v1.0
 288  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ def2-TZVPP v1.0
 289  torsiondrive                3848  OpenFF-benchmark-ligand-fragments-v1.0
 290  optimization                6716  OpenFF Protein Fragments v2.0
 291  torsiondrive                 845  OpenFF Protein Fragments TorsionDrives v1.0
 296  optimization               85897  OpenFF Sandbox CHO PhAlkEthOH v1.0
 297  optimization                6716  OpenFF Protein Peptide Fragments constrained v1.0
 298  optimization                6709  OpenFF Protein Peptide Fragments unconstrained v1.0
 299  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ 6-31+Gss
 300  torsiondrive                  36  OpenFF Theory Benchmarking Set B3LYP-D3BJ def2-TZVPPD
 301  optimization               22680  OpenFF PEPCONF OptimizationDataset v1.0
 302  torsiondrive                 780  OpenFF Substituted Phenyl Set 1 v2.0
 303  singlepoint                  382  OpenFF BCC Refit Study COH v1.0
 304  optimization                1376  OpenFF Roche Opt Set With Protomers and Tautomers v1.0
 305  optimization                 445  Genentech PDB Ligand Expo whole optimization neutral v1.0
 307  optimization                1376  OpenFF Roche Opt Set With Protomers and Tautomers v1.1
 308  torsiondrive                 787  OpenFF WBO Conjugated Series v1.0
 309  torsiondrive                 767  OpenFF Theory Benchmarking Set v1.0
 310  optimization                 864  OpenFF Theory Benchmarking Constrained Optimization Set B3LYP-NL def2-TZVPD v1.0
 311  optimization                 864  OpenFF Theory Benchmarking Constrained Optimization Set MP2 heavy-aug-cc-pVTZ v1.0
 312  optimization                 864  OpenFF Theory Benchmarking Constrained Optimization Set WB97X-V def2-TZVPD v1.0
 313  optimization                2363  Genentech PDB Ligand Expo fragment optimization neutral v1.0
 314  torsiondrive                 260  OpenFF Amide Torsion Set v1.0
 315  optimization                 223  OpenFF Aniline Para Opt v1.0
 316  optimization               69672  OpenFF Industry Benchmark Season 1 v1.0
 317  torsiondrive                 888  OpenFF Gen3 Torsion Set v1.0
 318  torsiondrive                  24  OpenFF Aniline 2D Impropers v1.0
 319  optimization              539385  OpenFF Industry Benchmark Season 1 v1.1
 320  optimization                4650  OpenFF BCC Refit Study COH v2.0
 321  optimization                1416  OpenFF Theory Benchmarking Constrained Optimization Set MP2 heavy-aug-cc-pVTZ v1.1
 322  optimization              501585  OpenFF Industry Benchmark Season 1 - MM v1.1
 323  singlepoint                66552  OpenFF Theory Benchmarking Single Point Energies v1.0
 324  torsiondrive                8052  OpenFF-benchmark-ligand-fragments-v2.0
 326  optimization                 735  OpenFF RESP Polarizability Optimizations v1.0
 327  optimization                 735  OpenFF RESP Polarizability Optimizations v1.1
 329  singlepoint                24000  TorsionNet500 Single Points Dataset v1.0
 333  torsiondrive                   5  OpenFF Protein Dipeptide 2-D TorsionDrive v1.0
 344  torsiondrive                   5  OpenFF Protein Dipeptide 2-D TorsionDrive v1.1
 345  optimization                 600  OpenFF Gen2 Optimization Dataset Protomers v1.0
 346  torsiondrive                  26  OpenFF Protein Dipeptide 2-D TorsionDrive v2.0
 347  singlepoint                65116  OpenFF ESP Fragment Conformers v1.0
 351  optimization               56054  OpenFF ESP Industry Benchmark Set v1.0
 352  torsiondrive                  46  OpenFF Protein Capped 1-mer Sidechains v1.0
 353  torsiondrive                  26  OpenFF Protein Dipeptide 2-D TorsionDrive v2.1
 357  singlepoint                39983  OpenFF ESP Industry Benchmark Set v1.1
 358  torsiondrive                  46  OpenFF Protein Capped 1-mer Sidechains v1.1
 359  torsiondrive                  46  OpenFF Protein Capped 1-mer Sidechains v1.2
 360  torsiondrive                  61  OpenFF Protein Capped 1-mer Sidechains v1.3
 363  optimization                 400  OpenFF multiplicity correction optimization set v1.0
 364  torsiondrive                  99  OpenFF multiplicity correction torsion drive data v1.0
 365  optimization                 759  OpenFF Protein Capped 1-mers 3-mers Optimization Dataset v1.0
 366  torsiondrive                  54  OpenFF Protein Capped 3-mer Backbones v1.0
 370  torsiondrive                 131  OpenFF multiplicity correction torsion drive data v1.1
 371  singlepoint                13467  RNA Single Point Dataset v1.0
 372  optimization                 327  OpenFF Iodine Chemistry Optimization Dataset v1.0
 373  singlepoint                81670  RNA Trinucleotide Single Point Dataset v1.0
 374  torsiondrive                  26  OpenFF Protein Capped 3-mer Omega v1.0
 375  singlepoint                19110  RNA Nucleoside Single Point Dataset v1.0
 376  optimization                 677  OpenFF multi-Br ESP Fragment Conformers v1.0
 377  singlepoint                  650  OpenFF multi-Br ESP Fragment Conformers v1.1
 378  torsiondrive                 169  XtalPi Shared Fragments TorsiondriveDataset v1.0
 379  optimization                2384  XtalPi Shared Fragments OptimizationDataset v1.0
 380  torsiondrive                  43  OpenFF Torsion Coverage Supplement v1.0
 381  torsiondrive                 192  OpenFF RNA Dinucleoside Monophosphate TorsionDrives v1.0
 382  torsiondrive                8737  XtalPi 20-percent Fragments TorsiondriveDataset v1.0
 383  optimization              128180  XtalPi 20-percent Fragments OptimizationDataset v1.0

Data in the QCArchive is organized into datasets. Let’s grab a molecule from an optimization dataset

dataset = client.get_dataset(
    dataset_type="optimization",
    dataset_name="Kinase Inhibitors: WBO Distributions",
)

Take an arbitrary entry from the collection.

entry = dataset.get_entry(entry_name=dataset.entry_names[-1])

We can view the entry in detail by looking at the dictionary representation.

entry.dict()
{'name': 'cs(=o)(=o)ccncc1ccc(o1)c2ccc3c(c2)c(ncn3)nc4ccc(c(c4)cl)occ5cccc(c5)f-99',
 'initial_molecule': {'schema_name': 'qcschema_molecule',
  'schema_version': 2,
  'validated': True,
  'symbols': array(['C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C',
         'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C',
         'C', 'C', 'C', 'N', 'N', 'N', 'N', 'O', 'O', 'O', 'O', 'F', 'S',
         'Cl', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H',
         'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H',
         'H'], dtype='<U2'),
  'geometry': array([[ 2.89795594e+01, -5.61363284e+00, -1.25287815e+00],
         [ 1.93871961e+00, -2.32111000e-01,  6.33108960e-01],
         [ 2.68259179e+01, -4.17201102e+00, -7.72020570e-01],
         [ 3.00076085e+00,  1.52774996e+00,  2.29260082e+00],
         [ 1.49570525e+01, -1.11862575e+00,  1.34408755e+00],
         [ 1.75606324e+01, -1.34772255e+00,  1.00269997e+00],
         [ 2.88520513e+01, -7.66237506e+00, -2.90660376e+00],
         [-1.45658410e-01, -4.97426507e+00, -2.31821525e+00],
         [ 3.61899100e-02, -6.76496484e+00, -4.29656036e+00],
         [ 5.84179420e+00, -2.55574391e+00,  6.16566210e-01],
         [ 2.44170261e+01, -6.82796240e+00, -3.59833171e+00],
         [ 1.47074969e+01, -5.54975040e+00,  2.41523984e+00],
         [ 8.88178633e+00,  2.63147751e+00,  5.47147212e+00],
         [ 6.89608538e+00, -7.77978130e-01,  2.28537091e+00],
         [ 3.36012318e+00, -2.27643411e+00, -2.05974190e-01],
         [ 2.45446332e+01, -4.77917198e+00, -1.94467087e+00],
         [ 5.47938551e+00,  1.29451410e+00,  3.14641803e+00],
         [ 1.35304415e+01, -3.21962903e+00,  2.05047930e+00],
         [ 1.87377014e+01, -3.67784426e+00,  1.36746581e+00],
         [ 2.65707774e+01, -8.26954593e+00, -4.07925220e+00],
         [ 1.73111436e+01, -5.77891513e+00,  2.07375878e+00],
         [ 2.26685911e+00, -4.09354036e+00, -1.91777351e+00],
         [ 2.54682332e+00, -6.85047933e+00, -4.96434856e+00],
         [ 9.36421367e+00, -9.47290490e-01,  3.17955455e+00],
         [ 4.24499629e+00, -9.67072630e+00, -1.91013351e+01],
         [ 2.22291900e+01, -3.22681771e+00, -1.42596726e+00],
         [ 3.96002561e+00, -8.30889479e+00, -6.85900096e+00],
         [ 4.53186197e+00, -9.37210543e+00, -1.12315297e+01],
         [ 3.74896337e+00, -8.84306732e+00, -1.39373610e+01],
         [ 6.46872465e+00,  3.00571708e+00,  4.74281203e+00],
         [ 1.04136881e+01,  7.31465300e-01,  4.77749054e+00],
         [ 1.09100724e+01, -2.98799013e+00,  2.39376886e+00],
         [ 3.09195245e+00, -7.87277816e+00, -9.41970199e+00],
         [ 8.13355678e+00, -9.67381434e+00, -1.60552111e+01],
         [ 4.99185979e+00, -1.32529008e+01, -1.57833896e+01],
         [ 3.91927510e+00, -5.22754616e+00, -3.52328384e+00],
         [ 2.12846153e+01, -3.84659395e+00,  1.02017540e+00],
         [ 2.64477766e+01, -1.02372453e+01, -5.66668056e+00],
         [ 5.54591265e+00, -1.05877799e+01, -1.61420006e+01],
         [ 1.87184396e+01, -8.68283521e+00,  2.53736164e+00],
         [ 3.07562580e+01, -5.14049891e+00, -3.40496340e-01],
         [-4.65003000e-03,  2.67554700e-02,  1.66408900e-02],
         [ 2.69431896e+01, -2.57930069e+00,  5.19680380e-01],
         [ 1.84816744e+00,  3.11097314e+00,  2.92148844e+00],
         [ 1.40590732e+01,  7.06064040e-01,  1.05468507e+00],
         [ 1.86602796e+01,  2.96905580e-01,  4.52302520e-01],
         [ 3.05285053e+01, -8.78494061e+00, -3.27991363e+00],
         [-1.83066261e+00, -4.40414485e+00, -1.31444107e+00],
         [-1.47796825e+00, -7.85402013e+00, -5.12826389e+00],
         [ 6.94015282e+00, -4.16145113e+00, -5.01537200e-02],
         [ 2.26487725e+01, -7.31482942e+00, -4.52177921e+00],
         [ 1.35866831e+01, -7.18299521e+00,  2.96635289e+00],
         [ 9.66759771e+00,  4.01679529e+00,  6.75738125e+00],
         [ 5.25555885e+00, -1.06711597e+01, -2.05945160e+01],
         [ 2.24609368e+00, -1.01756755e+01, -1.91179422e+01],
         [ 4.47789379e+00, -7.63297605e+00, -1.93109470e+01],
         [ 2.26644983e+01, -1.20419194e+00, -1.48882911e+00],
         [ 2.07621173e+01, -3.60554472e+00, -2.83618480e+00],
         [ 6.00251831e+00, -7.85696220e+00, -6.84065293e+00],
         [ 3.83567181e+00, -1.03722487e+01, -6.53978249e+00],
         [ 4.27006061e+00, -1.13641289e+01, -1.07410105e+01],
         [ 6.52448646e+00, -8.89050259e+00, -1.09607687e+01],
         [ 4.04834476e+00, -6.84946966e+00, -1.43964509e+01],
         [ 1.76271456e+00, -9.31303167e+00, -1.42681283e+01],
         [ 9.92112705e+00, -4.58168017e+00,  2.00994347e+00],
         [ 3.28745938e+00, -6.00243631e+00, -9.86032950e+00]]),
  'name': 'C29ClFH26N4O4S',
  'identifiers': {'molecule_hash': '58aaa33a5e7d9445e4276a015de257c3d6bde558',
   'molecular_formula': 'C29ClFH26N4O4S'},
  'molecular_charge': 0.0,
  'molecular_multiplicity': 1,
  'masses': array([12.        , 12.        , 12.        , 12.        , 12.        ,
         12.        , 12.        , 12.        , 12.        , 12.        ,
         12.        , 12.        , 12.        , 12.        , 12.        ,
         12.        , 12.        , 12.        , 12.        , 12.        ,
         12.        , 12.        , 12.        , 12.        , 12.        ,
         12.        , 12.        , 12.        , 12.        , 14.003074  ,
         14.003074  , 14.003074  , 14.003074  , 15.99491462, 15.99491462,
         15.99491462, 15.99491462, 18.99840316, 31.97207117, 34.96885268,
          1.00782503,  1.00782503,  1.00782503,  1.00782503,  1.00782503,
          1.00782503,  1.00782503,  1.00782503,  1.00782503,  1.00782503,
          1.00782503,  1.00782503,  1.00782503,  1.00782503,  1.00782503,
          1.00782503,  1.00782503,  1.00782503,  1.00782503,  1.00782503,
          1.00782503,  1.00782503,  1.00782503,  1.00782503,  1.00782503,
          1.00782503]),
  'real': array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True,  True,  True,  True,  True,  True,  True,
          True,  True,  True]),
  'atom_labels': array(['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
         '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
         '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '',
         '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''],
        dtype='<U1'),
  'atomic_numbers': array([ 6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,
          6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,  8,
          8,  8,  8,  9, 16, 17,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
          1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1],
        dtype=int16),
  'mass_numbers': array([12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
         12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 14, 14, 14, 14, 16,
         16, 16, 16, 19, 32, 35,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,
          1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1],
        dtype=int16),
  'connectivity': [(0, 2, 2.0),
   (0, 6, 1.0),
   (0, 40, 1.0),
   (1, 3, 2.0),
   (1, 14, 1.0),
   (1, 41, 1.0),
   (2, 15, 1.0),
   (2, 42, 1.0),
   (3, 16, 1.0),
   (3, 43, 1.0),
   (4, 5, 2.0),
   (4, 17, 1.0),
   (4, 44, 1.0),
   (5, 18, 1.0),
   (5, 45, 1.0),
   (6, 19, 2.0),
   (6, 46, 1.0),
   (7, 8, 1.0),
   (7, 21, 2.0),
   (7, 47, 1.0),
   (8, 22, 2.0),
   (8, 48, 1.0),
   (9, 13, 1.0),
   (9, 14, 2.0),
   (9, 49, 1.0),
   (10, 15, 2.0),
   (10, 19, 1.0),
   (10, 50, 1.0),
   (11, 17, 2.0),
   (11, 20, 1.0),
   (11, 51, 1.0),
   (12, 29, 1.0),
   (12, 30, 2.0),
   (12, 52, 1.0),
   (13, 16, 1.0),
   (13, 23, 2.0),
   (14, 21, 1.0),
   (15, 25, 1.0),
   (16, 29, 2.0),
   (17, 31, 1.0),
   (18, 20, 2.0),
   (18, 36, 1.0),
   (19, 37, 1.0),
   (20, 39, 1.0),
   (21, 35, 1.0),
   (22, 26, 1.0),
   (22, 35, 1.0),
   (23, 30, 1.0),
   (23, 31, 1.0),
   (24, 38, 1.0),
   (24, 53, 1.0),
   (24, 54, 1.0),
   (24, 55, 1.0),
   (25, 36, 1.0),
   (25, 56, 1.0),
   (25, 57, 1.0),
   (26, 32, 1.0),
   (26, 58, 1.0),
   (26, 59, 1.0),
   (27, 28, 1.0),
   (27, 32, 1.0),
   (27, 60, 1.0),
   (27, 61, 1.0),
   (28, 38, 1.0),
   (28, 62, 1.0),
   (28, 63, 1.0),
   (31, 64, 1.0),
   (32, 65, 1.0),
   (33, 38, 2.0),
   (34, 38, 2.0)],
  'fragments': [array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
          17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
          34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
          51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65],
         dtype=int32)],
  'fragment_charges': [0.0],
  'fragment_multiplicities': [1],
  'fix_com': True,
  'fix_orientation': True,
  'provenance': {'creator': 'QCElemental',
   'version': 'v0.11.1',
   'routine': 'qcelemental.molparse.from_schema'},
  'id': 9590661,
  'extras': {}},
 'additional_keywords': {},
 'attributes': {'inchi_key': 'BCFGMOOMADDAQU-UHFFFAOYSA-N',
  'provenance': 'cmiles_v0.1.5+1.gdbd63e8_openeye_2019.Apr.2',
  'standard_inchi': 'InChI=1S/C29H26ClFN4O4S/c1-40(36,37)12-11-32-16-23-7-10-27(39-23)20-5-8-26-24(14-20)29(34-18-33-26)35-22-6-9-28(25(30)15-22)38-17-19-3-2-4-21(31)13-19/h2-10,13-15,18,32H,11-12,16-17H2,1H3,(H,33,34,35)',
  'canonical_smiles': 'CS(=O)(=O)CCNCc1ccc(o1)c2ccc3c(c2)c(ncn3)Nc4ccc(c(c4)Cl)OCc5cccc(c5)F',
  'molecular_formula': 'C29H26ClFN4O4S',
  'canonical_isomeric_smiles': 'CS(=O)(=O)CCNCc1ccc(o1)c2ccc3c(c2)c(ncn3)Nc4ccc(c(c4)Cl)OCc5cccc(c5)F',
  'unique_protomer_representation': 'CS(=O)(=O)CCNCc1ccc(o1)c2ccc3c(c2)c(ncn3)Nc4ccc(c(c4)Cl)OCc5cccc(c5)F',
  'unique_tautomer_representation': 'CS(=O)(=O)CCNCc1ccc(-c2ccc3[nH]cnc(=Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1',
  'canonical_explicit_hydrogen_smiles': '[H]c1c(c(c(c(c1[H])F)[H])C([H])([H])Oc2c(c(c(c(c2Cl)[H])N([H])c3c4c(c(c(c(c4nc(n3)[H])[H])[H])c5c(c(c(o5)C([H])([H])N([H])C([H])([H])C([H])([H])S(=O)(=O)C([H])([H])[H])[H])[H])[H])[H])[H])[H]',
  'canonical_isomeric_explicit_hydrogen_smiles': '[H]c1c(c(c(c(c1[H])F)[H])C([H])([H])Oc2c(c(c(c(c2Cl)[H])N([H])c3c4c(c(c(c(c4nc(n3)[H])[H])[H])c5c(c(c(o5)C([H])([H])N([H])C([H])([H])C([H])([H])S(=O)(=O)C([H])([H])[H])[H])[H])[H])[H])[H])[H]',
  'canonical_isomeric_explicit_hydrogen_mapped_smiles': '[H:41][c:1]1[c:3]([c:16]([c:11]([c:20]([c:7]1[H:47])[F:38])[H:51])[C:26]([H:57])([H:58])[O:37][c:19]2[c:6]([c:5]([c:18]([c:12]([c:21]2[Cl:40])[H:52])[N:32]([H:65])[c:24]3[c:14]4[c:10]([c:15]([c:2]([c:4]([c:17]4[n:30][c:13]([n:31]3)[H:53])[H:44])[H:42])[c:22]5[c:8]([c:9]([c:23]([o:36]5)[C:27]([H:59])([H:60])[N:33]([H:66])[C:28]([H:61])([H:62])[C:29]([H:63])([H:64])[S:39](=[O:34])(=[O:35])[C:25]([H:54])([H:55])[H:56])[H:49])[H:48])[H:50])[H:45])[H:46])[H:43]'},
 'comment': None}

Now we can make a molecule using a few different input options.

# first make a molecule using this record object
molecule_from_entry = Molecule.from_qcschema(entry)

# we could have also used the dictionary representation of the object
molecule_from_dict = Molecule.from_qcschema(entry.dict())

assert molecule_from_entry == molecule_from_dict

molecule = molecule_from_entry
# first let's get the initial molecule from the database
initial_molecule = client.get_molecules(entry.initial_molecule.id)

# note that this molecule uses an object model from QCArchive, _not_ the toolkit
print(type(initial_molecule))

# we check that the molecule has been ordered to match the ordering used in the data base
# by printing out the atomic numbers of both objects in order

for atoms in zip(molecule.atoms, initial_molecule.atomic_numbers):
    print(atoms[0].atomic_number, atoms[1])
    assert atoms[0].atomic_number == atoms[1]

# can compare other things, too
print(molecule.to_hill_formula(), initial_molecule.get_molecular_formula())

# QCArchive molecules don't store all information the
# toolkit needs, like bond orders and formal charges;
# that's why there is a Molecule.from_qcschema() method at all
<class 'qcelemental.models.molecule.Molecule'>
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
6 6
7 7
7 7
7 7
7 7
8 8
8 8
8 8
8 8
9 9
16 16
17 17
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
C29H26ClFN4O4S C29ClFH26N4O4S
# we can also compare the graph representations of the molecules to make sure they are in the same order
import networkx as nx

# make a graph of the initial molecule using newtorkx and the data in the record
initial_network = nx.Graph()
for index, atomic_number in enumerate(initial_molecule.atomic_numbers):
    initial_network.add_node(index, atomic_number=atomic_number)

for bond in initial_molecule.connectivity:
    initial_network.add_edge(*bond[:2])
# now we can use the new isomorphic check to get the atom mapping
isomorphic, atom_map = Molecule.are_isomorphic(
    molecule,
    initial_network,
    return_atom_map=True,
    aromatic_matching=False,
    formal_charge_matching=False,
    bond_order_matching=False,
    bond_stereochemistry_matching=False,
    atom_stereochemistry_matching=False,
)

# we can check if the graph was found to be isomorphic and whether or not the
# atom mappings are in the same order
assert isomorphic
print(atom_map)
for index1, index2 in atom_map.items():
    assert index1 == index2
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65}
mol = Molecule.from_qcschema(entry)
mol
# OpenFF Toolkit `Molecule` objects can be converted back into QCArchive molecules,
# as long as there are conformer(s)

qc_molecule = molecule.to_qcschema()

qc_molecule

This transformation unlocks functionality of QCEngine (computing energies, gradients, hessians, etc. with a variety of different methods).

Here we will try and compute the energy using RDKit (only run this cell if QCEngine is installed.)

import qcengine

# set up the RDKit task
rdkit_task = {
    "schema_name": "qcschema_input",
    "schema_version": 2,
    "molecule": qc_molecule,
    "driver": "energy",
    "model": {"method": "uff", "basis": None},
    "keywords": {"scf_type": "df"},
}

# now lets compute the energy using qcengine and RDKit and print the result
result = qcengine.compute(rdkit_task, "rdkit")
# note the result is in QC units of hartrees
print(result.return_result)
0.05930479138457709