diff --git a/chai_lab/data/dataset/inference_dataset.py b/chai_lab/data/dataset/inference_dataset.py
index 4b04f8f..7ac2fd7 100644
--- a/chai_lab/data/dataset/inference_dataset.py
+++ b/chai_lab/data/dataset/inference_dataset.py
@@ -126,8 +126,19 @@ def raw_inputs_to_entitites_data(
         assert residues is not None
 
         # Determine the entity id (unique integer for each distinct sequence)
-        # NOTE very important for recognizing things like homo polymers
-        seq: tuple[str, ...] = tuple(res.name for res in residues)
+        # NOTE because ligand residues have a single "LIG" residue name, the name field
+        # cannot be used to distinguish them. Instead, we use the sequence field itself,
+        # which should contain the SMILES string. This is not ideal, as it fails to
+        # distinguish betwen different SMILES strings that represent the same molecule,
+        # but should capture most cases.
+        # We do not need to do special check on glycans because they are specified as a
+        # string of monosaccharides, which behaves similarly to a string of amino acid
+        # residues.
+        seq: tuple[str, ...] = (
+            (input.sequence,)
+            if input.entity_type == EntityType.LIGAND.value
+            else tuple(res.name for res in residues)
+        )
         entity_key: tuple[EntityType, tuple[str, ...]] = (entity_type, seq)
         if entity_key in entity_to_index:
             entity_id = entity_to_index[entity_key]
diff --git a/tests/test_inference_dataset.py b/tests/test_inference_dataset.py
index 6269351..c769eb8 100644
--- a/tests/test_inference_dataset.py
+++ b/tests/test_inference_dataset.py
@@ -7,11 +7,16 @@
 """
 
 import pytest
+import torch
 
 from chai_lab.data.dataset.inference_dataset import Input, load_chains_from_raw
 from chai_lab.data.dataset.structure.all_atom_residue_tokenizer import (
     AllAtomResidueTokenizer,
 )
+from chai_lab.data.dataset.structure.all_atom_structure_context import (
+    AllAtomStructureContext,
+)
+from chai_lab.data.dataset.structure.chain import Chain
 from chai_lab.data.parsing.structure.entity_type import EntityType
 from chai_lab.data.sources.rdkit import RefConformerGenerator
 
@@ -53,3 +58,49 @@ def test_ions_parsing(tokenizer: AllAtomResidueTokenizer):
     assert chain.structure_context.num_atoms == 1
     assert chain.structure_context.atom_ref_charge == 2
     assert chain.structure_context.atom_ref_element.item() == 12
+
+
+def test_protein_with_smiles(tokenizer: AllAtomResidueTokenizer):
+    """Complex with multiple duplicated protein chains and SMILES ligands."""
+    # Based on https://www.rcsb.org/structure/1AFS
+    seq = "MDSISLRVALNDGNFIPVLGFGTTVPEKVAKDEVIKATKIAIDNGFRHFDSAYLYEVEEEVGQAIRSKIEDGTVKREDIFYTSKLWSTFHRPELVRTCLEKTLKSTQLDYVDLYIIHFPMALQPGDIFFPRDEHGKLLFETVDICDTWEAMEKCKDAGLAKSIGVSNFNCRQLERILNKPGLKYKPVCNQVECHLYLNQSKMLDYCKSKDIILVSYCTLGSSRDKTWVDQKSPVLLDDPVLCAIAKKYKQTPALVALRYQLQRGVVPLIRSFNAKRIKELTQVFEFQLASEDMKALDGLNRNFRYNNAKYFDDHPNHPFTDEN"
+    nap = "NC(=O)c1ccc[n+](c1)[CH]2O[CH](CO[P]([O-])(=O)O[P](O)(=O)OC[CH]3O[CH]([CH](O[P](O)(O)=O)[CH]3O)n4cnc5c(N)ncnc45)[CH](O)[CH]2O"
+    tes = "O=C4C=C3C(C2CCC1(C(CCC1O)C2CC3)C)(C)CC4"
+    inputs = [
+        Input(seq, EntityType.PROTEIN.value, entity_name="A"),
+        Input(seq, EntityType.PROTEIN.value, entity_name="B"),
+        Input(nap, EntityType.LIGAND.value, entity_name="C"),
+        Input(nap, EntityType.LIGAND.value, entity_name="D"),
+        Input(tes, EntityType.LIGAND.value, entity_name="E"),
+        Input(tes, EntityType.LIGAND.value, entity_name="F"),
+    ]
+    chains: list[Chain] = load_chains_from_raw(inputs, tokenizer=tokenizer)
+    assert len(chains) == len(inputs)
+
+    example = AllAtomStructureContext.merge(
+        [chain.structure_context for chain in chains]
+    )
+
+    # Should be 1 protein chain, 2 ligand chains
+    assert example.token_entity_id.unique().numel() == 3
+    assert example.token_asym_id.unique().numel() == 6
+
+    # Check protein chains
+    prot_entity_ids = example.token_entity_id[
+        example.token_entity_type == EntityType.PROTEIN.value
+    ]
+    assert torch.unique(prot_entity_ids).numel() == 1
+    prot_sym_ids = example.token_sym_id[
+        example.token_entity_type == EntityType.PROTEIN.value
+    ]
+    assert torch.unique(prot_sym_ids).numel() == 2  # Two copies of this chain
+
+    # Check ligand chains
+    lig_entity_ids = example.token_entity_id[
+        example.token_entity_type == EntityType.LIGAND.value
+    ]
+    assert torch.unique(lig_entity_ids).numel() == 2
+    lig_sym_ids = example.token_sym_id[
+        example.token_entity_type == EntityType.LIGAND.value
+    ]
+    assert torch.unique(lig_sym_ids).numel() == 2  # Two copies of each ligand