Split mol_to_smiles into a util

descent/targets/dimers.py

@@ -7,11 +7,11 @@
 import smee.utils
 import torch
 
+import descent.utils.molecule
 import descent.utils.reporting
 
 if typing.TYPE_CHECKING:
     import pandas
-    from rdkit import Chem
 
 
 EnergyFn = typing.Callable[
@@ -42,11 +42,11 @@ class Dimer(typing.TypedDict):
     source: str
 
 
-def create_dataset(entries: list[Dimer]) -> pyarrow.Table:
+def create_dataset(dimers: list[Dimer]) -> pyarrow.Table:
     """Create a dataset from a list of existing dimers.
 
     Args:
-        entries: The dimers to create the dataset from.
+        dimers: The dimers to create the dataset from.
 
     Returns:
         The created dataset.
@@ -55,37 +55,18 @@ def create_dataset(entries: list[Dimer]) -> pyarrow.Table:
     return pyarrow.Table.from_pylist(
         [
             {
-                "smiles_a": entry["smiles_a"],
-                "smiles_b": entry["smiles_b"],
-                "coords": torch.tensor(entry["coords"]).flatten().tolist(),
-                "energy": torch.tensor(entry["energy"]).flatten().tolist(),
-                "source": entry["source"],
+                "smiles_a": dimer["smiles_a"],
+                "smiles_b": dimer["smiles_b"],
+                "coords": torch.tensor(dimer["coords"]).flatten().tolist(),
+                "energy": torch.tensor(dimer["energy"]).flatten().tolist(),
+                "source": dimer["source"],
             }
-            for entry in entries
+            for dimer in dimers
         ],
         schema=DATA_SCHEMA,
     )
 
 
-def _mol_to_smiles(mol: "Chem.Mol") -> str:
-    """Convert a molecule to a SMILES string with atom mapping.
-
-    Args:
-        mol: The molecule to convert.
-
-    Returns:
-        The SMILES string.
-    """
-    from rdkit import Chem
-
-    mol = Chem.AddHs(mol)
-
-    for atom in mol.GetAtoms():
-        atom.SetAtomMapNum(atom.GetIdx() + 1)
-
-    return Chem.MolToSmiles(mol)
-
-
 def create_from_des(
     data_dir: pathlib.Path,
     energy_fn: EnergyFn,
@@ -109,7 +90,7 @@ def create_from_des(
     metadata = pandas.read_csv(data_dir / f"{data_dir.name}.csv", index_col=False)
 
     system_ids = metadata["system_id"].unique()
-    entries: list[Dimer] = []
+    dimers: list[Dimer] = []
 
     for system_id in system_ids:
         system_data = metadata[metadata["system_id"] == system_id]
@@ -128,8 +109,8 @@ def create_from_des(
             )
             mol_a, mol_b = Chem.GetMolFrags(dimer_example, asMols=True)
 
-            smiles_a = _mol_to_smiles(mol_a)
-            smiles_b = _mol_to_smiles(mol_b)
+            smiles_a = descent.utils.molecule.mol_to_smiles(mol_a, False)
+            smiles_b = descent.utils.molecule.mol_to_smiles(mol_b, False)
 
             source = (
                 f"{data_dir.name} system={system_id} orig={group_orig} group={group_id}"
@@ -149,7 +130,7 @@ def create_from_des(
             coords = torch.tensor(coords_raw)
             energy = energy_fn(group_data, geometry_ids, coords)
 
-            entries.append(
+            dimers.append(
                 {
                     "smiles_a": smiles_a,
                     "smiles_b": smiles_b,
@@ -159,7 +140,7 @@ def create_from_des(
                 }
             )
 
-    return create_dataset(entries)
+    return create_dataset(dimers)
 
 
 def extract_smiles(dataset: pyarrow.Table) -> list[str]:
@@ -328,17 +309,17 @@ def report(
 
     rows = []
 
-    for entry in dataset.to_pylist():
-        energies = {"ref": torch.tensor(entry["energy"])}
+    for dimer in dataset.to_pylist():
+        energies = {"ref": torch.tensor(dimer["energy"])}
         energies.update(
-            (force_field_name, _predict(entry, force_field, topologies)[1])
+            (force_field_name, _predict(dimer, force_field, topologies)[1])
             for force_field_name, force_field in force_fields.items()
         )
 
         plot_img = _plot_energies(energies)
 
         mol_img = descent.utils.reporting.mols_to_img(
-            entry["smiles_a"], entry["smiles_b"]
+            dimer["smiles_a"], dimer["smiles_b"]
         )
         rows.append({"Dimer": mol_img, "Energy [kcal/mol]": plot_img})
 

descent/tests/utils/test_molecule.py

@@ -0,0 +1,18 @@
+import pytest
+from rdkit import Chem
+
+from descent.utils.molecule import mol_to_smiles
+
+
+@pytest.mark.parametrize(
+    "input_smiles, expected_smiles, canonical",
+    [
+        ("OC", "[H:1][C:4]([H:2])([O:3][H:5])[H:6]", True),
+        ("OC", "[O:1]([C:2]([H:4])([H:5])[H:6])[H:3]", False),
+    ],
+)
+def test_mol_to_smiles(input_smiles, expected_smiles, canonical):
+    mol = Chem.MolFromSmiles(input_smiles)
+    actual_smiles = mol_to_smiles(mol, canonical)
+
+    assert actual_smiles == expected_smiles

descent/utils/molecule.py

@@ -0,0 +1,29 @@
+import typing
+
+if typing.TYPE_CHECKING:
+    from rdkit import Chem
+
+
+def mol_to_smiles(mol: "Chem.Mol", canonical: bool = True) -> str:
+    """Convert a molecule to a SMILES string with atom mapping.
+
+    Args:
+        mol: The molecule to convert.
+        canonical: Whether to canonicalize the atom ordering prior to assigning
+            map indices.
+
+    Returns:
+        The SMILES string.
+    """
+    from rdkit import Chem
+
+    mol = Chem.AddHs(mol)
+
+    if canonical:
+        order = Chem.CanonicalRankAtoms(mol, includeChirality=True)
+        mol = Chem.RenumberAtoms(mol, list(order))
+
+    for atom in mol.GetAtoms():
+        atom.SetAtomMapNum(atom.GetIdx() + 1)
+
+    return Chem.MolToSmiles(mol)