selfies - npm Package Compare versions

Comparing version 2.1.2 to 2.2.0

PKG-INFO

 Metadata-Version: 2.1
 Name: selfies
-Version: 2.1.2
+Version: 2.2.0
 Summary: SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs.
 Home-page: https://github.com/aspuru-guzik-group/selfies
-Author: Mario Krenn, Alston Lo, and many other contributors
-Author-email: [e-mail], [e-mail]
+Author: Mario Krenn, Alston Lo, Robert Pollice and many other contributors
+Author-email: [e-mail], [e-mail]
 Classifier: Programming Language :: Python :: 3
@@ -81,3 +81,3 @@ Classifier: Programming Language :: Python :: 3.7
 
-Please refer to the [documentation](https://selfiesv2.readthedocs.io/en/latest/),
+Please refer to the [documentation in our code-paper](https://pubs.rsc.org/en/content/articlelanding/2023/DD/D3DD00044C),
 which contains a thorough tutorial  for getting started with ``selfies``
@@ -244,8 +244,7 @@ and detailed descriptions of the functions
  * 50K molecules from a dataset of [non-fullerene acceptors for organic solar cells](https://www.sciencedirect.com/science/article/pii/S2542435117301307)
- * 160K+ molecules from various [MoleculeNet](http://moleculenet.ai/datasets-1) datasets
- * 36M+ molecules from the [eMolecules Database](https://www.emolecules.com/info/products-data-downloads.html).
-   Due to its large size, this dataset is not included on the repository. To run tests
-   on it, please download the dataset into the ``tests/test_sets`` directory
-   and run the ``tests/run_on_large_dataset.py`` script.
+ * 160K+ molecules from various [MoleculeNet](https://moleculenet.org/datasets-1) datasets
 
+In first releases, we also tested the 36M+ molecules from the [eMolecules Database](https://downloads.emolecules.com/free/2024-12-01/).
+
+
 ## Version History
@@ -252,0 +251,0 @@ See [CHANGELOG](https://github.com/aspuru-guzik-group/selfies/blob/master/CHANGELOG.md).

README.md

@@ -62,3 +62,3 @@ # SELFIES
 
-Please refer to the [documentation](https://selfiesv2.readthedocs.io/en/latest/),
+Please refer to the [documentation in our code-paper](https://pubs.rsc.org/en/content/articlelanding/2023/DD/D3DD00044C),
 which contains a thorough tutorial  for getting started with ``selfies``
@@ -225,8 +225,7 @@ and detailed descriptions of the functions
  * 50K molecules from a dataset of [non-fullerene acceptors for organic solar cells](https://www.sciencedirect.com/science/article/pii/S2542435117301307)
- * 160K+ molecules from various [MoleculeNet](http://moleculenet.ai/datasets-1) datasets
- * 36M+ molecules from the [eMolecules Database](https://www.emolecules.com/info/products-data-downloads.html).
-   Due to its large size, this dataset is not included on the repository. To run tests
-   on it, please download the dataset into the ``tests/test_sets`` directory
-   and run the ``tests/run_on_large_dataset.py`` script.
+ * 160K+ molecules from various [MoleculeNet](https://moleculenet.org/datasets-1) datasets
 
+In first releases, we also tested the 36M+ molecules from the [eMolecules Database](https://downloads.emolecules.com/free/2024-12-01/).
+
+
 ## Version History
@@ -233,0 +232,0 @@ See [CHANGELOG](https://github.com/aspuru-guzik-group/selfies/blob/master/CHANGELOG.md).

selfies.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: selfies
-Version: 2.1.2
+Version: 2.2.0
 Summary: SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs.
 Home-page: https://github.com/aspuru-guzik-group/selfies
-Author: Mario Krenn, Alston Lo, and many other contributors
-Author-email: [e-mail], [e-mail]
+Author: Mario Krenn, Alston Lo, Robert Pollice and many other contributors
+Author-email: [e-mail], [e-mail]
 Classifier: Programming Language :: Python :: 3
@@ -81,3 +81,3 @@ Classifier: Programming Language :: Python :: 3.7
 
-Please refer to the [documentation](https://selfiesv2.readthedocs.io/en/latest/),
+Please refer to the [documentation in our code-paper](https://pubs.rsc.org/en/content/articlelanding/2023/DD/D3DD00044C),
 which contains a thorough tutorial  for getting started with ``selfies``
@@ -244,8 +244,7 @@ and detailed descriptions of the functions
  * 50K molecules from a dataset of [non-fullerene acceptors for organic solar cells](https://www.sciencedirect.com/science/article/pii/S2542435117301307)
- * 160K+ molecules from various [MoleculeNet](http://moleculenet.ai/datasets-1) datasets
- * 36M+ molecules from the [eMolecules Database](https://www.emolecules.com/info/products-data-downloads.html).
-   Due to its large size, this dataset is not included on the repository. To run tests
-   on it, please download the dataset into the ``tests/test_sets`` directory
-   and run the ``tests/run_on_large_dataset.py`` script.
+ * 160K+ molecules from various [MoleculeNet](https://moleculenet.org/datasets-1) datasets
 
+In first releases, we also tested the 36M+ molecules from the [eMolecules Database](https://downloads.emolecules.com/free/2024-12-01/).
+
+
 ## Version History
@@ -252,0 +251,0 @@ See [CHANGELOG](https://github.com/aspuru-guzik-group/selfies/blob/master/CHANGELOG.md).

selfies/bond_constraints.py

@@ -12,5 +12,5 @@ import functools
     "N": 3, "N+1": 4, "N-1": 2,
-    "C": 4, "C+1": 5, "C-1": 3,
-    "P": 5, "P+1": 6, "P-1": 4,
-    "S": 6, "S+1": 7, "S-1": 5,
+    "C": 4, "C+1": 3, "C-1": 3,
+    "P": 5, "P+1": 4, "P-1": 6,
+    "S": 6, "S+1": 5, "S-1": 5,
     "?": 8
@@ -52,3 +52,3 @@ }
         +-----------------+-----------+---+---+-----+-----+---+-----+-----+
-        | ``default``     |     1     | 3 | 5 |  6  |  4  | 6 |  7  |  5  |
+        | ``default``     |     1     | 3 | 5 |  4  |  6  | 6 |  5  |  5  |
         +-----------------+-----------+---+---+-----+-----+---+-----+-----+
@@ -55,0 +55,0 @@ | ``octet_rule``  |     1     | 3 | 3 |  4  |  2  | 2 |  3  |  1  |

selfies/constants.py

@@ -24,2 +24,9 @@ ELEMENTS = {
 
+VALENCE_ELECTRONS = {
+    "B": 3, "Al": 3,
+    "C": 4, "Si": 4,
+    "N": 5, "P": 5, "As": 5,
+    "O": 6, "S": 6, "Se": 6, "Te": 6
+}
+
 AROMATIC_SUBSET = set(e.lower() for e in AROMATIC_VALENCES)
@@ -26,0 +33,0 @@

selfies/mol_graph.py

@@ -7,3 +7,3 @@ import functools
 from selfies.bond_constraints import get_bonding_capacity
-from selfies.constants import AROMATIC_VALENCES
+from selfies.constants import AROMATIC_VALENCES, VALENCE_ELECTRONS
 from selfies.utils.matching_utils import find_perfect_matching
@@ -258,3 +258,3 @@
         kept_nodes = set(itertools.filterfalse(self._prune_from_ds, ds))
-
+        
         # relabel kept DS nodes to be 0, 1, 2, ...
@@ -270,3 +270,3 @@ label_to_node = list(sorted(kept_nodes))
                 pruned_ds[label].append(node_to_label[adj])
-
+        
         matching = find_perfect_matching(pruned_ds)
@@ -294,9 +294,9 @@ if matching is None:
             return True  # aromatic atom with no aromatic bonds
-
+        
         atom = self._atoms[node]
         valences = AROMATIC_VALENCES[atom.element]
-
+        
         # each bond in DS has order 1.5 - we treat them as single bonds
         used_electrons = int(self._bond_counts[node] - 0.5 * len(adj_nodes))
-
+        
         if atom.h_count is None:  # account for implicit Hs
@@ -308,3 +308,18 @@ assert atom.charge == 0
             used_electrons += atom.h_count
-            free_electrons = valence - used_electrons
-            return not ((free_electrons >= 0) and (free_electrons % 2 != 0))
+            
+            # count the total number of bound electrons of each atom
+            bound_electrons = (max(0, atom.charge) + atom.h_count 
+                               + int(self._bond_counts[node]) 
+                               + int(2 * (self._bond_counts[node] % 1)))
+            
+            # calculate the number of unpaired electrons of each atom
+            radical_electrons = (max(0, VALENCE_ELECTRONS[atom.element] 
+                                 - bound_electrons) % 2)
+            
+            # unpaired electrons do not contribute to the aromatic system
+            free_electrons = valence - used_electrons - radical_electrons
+            
+            if any(used_electrons == v - atom.charge for v in valences):
+                return True
+            else:
+                return not ((free_electrons >= 0) and (free_electrons % 2 != 0))

setup.py

@@ -10,5 +10,5 @@ #!/usr/bin/env python
     name="selfies",
-    version="2.1.2",
-    author="Mario Krenn, Alston Lo, and many other contributors",
-    author_email="[e-mail], [e-mail]",
+    version="2.2.0",
+    author="Mario Krenn, Alston Lo, Robert Pollice and many other contributors",
+    author_email="[e-mail], [e-mail]",
     description="SELFIES (SELF-referencIng Embedded Strings) is a "
@@ -15,0 +15,0 @@ "general-purpose, sequence-based, robust representation of "

tests/test_specific_cases.py

@@ -11,2 +11,8 @@ import pytest
 
+def roundtrip_eq(smiles_in, smiles_out):
+    sel = sf.encoder(smiles_in)
+    smi = sf.decoder(sel)
+    return smi == smiles_out
+
+
 def test_branch_and_ring_at_state_X0():
@@ -334,2 +340,3 @@ """Tests SELFIES with branches and rings at state X0 (i.e. at the
 
+
 def test_large_selfies_decoding():
@@ -342,2 +349,23 @@ """Test that we can decode extremely large SELFIES strings (used to cause a RecursionError)
 
-    assert decode_eq(large_selfies, expected_smiles)
+    assert decode_eq(large_selfies, expected_smiles)
+
+
+def test_radical_kekulization():
+    """Tests kekulization of aromatic systems with radicals and charges.
+    """
+    
+    assert roundtrip_eq("c1ccc[c]c1", "C1=CC=C[CH0]=C1")
+    assert roundtrip_eq("c1[c]n1(C)", "C1=[CH0]N1C")
+    assert roundtrip_eq("c1[C][n+]1(C)", "C=1[CH0][N+1]=1C")
+    assert roundtrip_eq("c1nnn[n-]1", "C1=NN=N[N-1]1")
+    assert roundtrip_eq("c1ccn[c-](C)[n+]1=O", "C1=CC=N[C-1](C)[N+1]1=O")
+    assert roundtrip_eq("c1ccs[n+]1c2ccccc2", "C=1C=CS[N+1]=1C2=CC=CC=C2")
+    assert roundtrip_eq("c1ccs[nH+]1", "C=1C=CS[NH1+1]=1")
+    
+    
+def test_novel_charged_symbols():
+    """Test decoding of updated constraints for charged atoms (update in 2.2.0)."""
+    assert decode_eq("[N][#C+1][#NH1][#C@H1]", "N#[C+1]")
+    assert decode_eq("[O+1][=P+1][#P-1][#C@@]", "[O+1]=[P+1]=[P-1]#[C@@]")
+    assert decode_eq("[=C-1][#S+1][#B]", "[C-1]#[S+1]=B")
+

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