feat(validation): enforce single-anchor fragments

- skip fused/shared/multi-anchor side systems during extraction
- add fragment library schema and fragment_library.csv export
- make scaffold prep strict for non-spliceable positions

tests/validation/test_validator.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import json
+import sqlite3
+
+import pandas as pd
+
+from macro_lactone_toolkit.validation.validator import MacrolactoneValidator
+
+from ..helpers import build_macrolactone_with_fused_side_ring
+
+
+def test_validator_exports_only_single_anchor_fragments_and_fragment_library(tmp_path):
+    built = build_macrolactone_with_fused_side_ring(side_chains={10: "methyl"})
+    input_path = tmp_path / "input.csv"
+    output_dir = tmp_path / "validation_output"
+
+    pd.DataFrame(
+        [
+            {
+                "ml_id": "ML00000001",
+                "IDs": "CHEMBL0001",
+                "smiles": built.smiles,
+            }
+        ]
+    ).to_csv(input_path, index=False)
+
+    validator = MacrolactoneValidator(output_dir=output_dir, sample_ratio=1.0)
+    results = validator.run(input_path)
+
+    assert results == {"total": 1, "success": 1, "failed": 0, "skipped": 0}
+
+    with sqlite3.connect(output_dir / "fragments.db") as connection:
+        fragments = connection.execute(
+            "SELECT cleavage_position, has_dummy_atom, dummy_atom_count FROM side_chain_fragments"
+        ).fetchall()
+        library_entries = connection.execute(
+            """
+            SELECT source_type, source_parent_ml_id, source_parent_chembl_id,
+                   cleavage_position, has_dummy_atom, dummy_atom_count, splice_ready
+            FROM fragment_library_entries
+            """
+        ).fetchall()
+
+    assert fragments == [(10, 1, 1)]
+    assert library_entries == [("validation_extract", "ML00000001", "CHEMBL0001", 10, 1, 1, 1)]
+
+    summary = pd.read_csv(output_dir / "summary.csv")
+    assert summary.loc[0, "num_sidechains"] == 1
+    assert json.loads(summary.loc[0, "cleavage_positions"]) == [10]
+
+    fragment_library = pd.read_csv(output_dir / "fragment_library.csv")
+    assert fragment_library.loc[0, "source_type"] == "validation_extract"
+    assert int(fragment_library.loc[0, "cleavage_position"]) == 10
+    assert bool(fragment_library.loc[0, "has_dummy_atom"]) is True
+    assert int(fragment_library.loc[0, "dummy_atom_count"]) == 1
+    assert bool(fragment_library.loc[0, "splice_ready"]) is True