feat(toolkit): add classification and migration

Implement the standard/non-standard/not-macrolactone classification layer
and integrate it into analyzer, fragmenter, and CLI outputs.

Port the remaining legacy package capabilities into new visualization and
workflow modules, restore batch/statistics/SDF scripts on top of the flat
CSV workflow, and update active docs to the new package API.

scripts/generate_sdf_and_statistics.py

@@ -1,11 +1,56 @@
-from macro_lactone_toolkit import FragmentationResult
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import AllChem
 
 
-def main() -> None:
-    raise SystemExit(
-        "Legacy helper retired. Use 'macro-lactone-toolkit fragment' to export fragments, "
-        "then generate SDF/statistics in downstream analysis code."
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Generate cleavage statistics and ETKDGv3 parent-molecule SDF files from flat fragment CSV."
     )
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--output-dir", required=True)
+    return parser
+
+
+def main(argv: list[str] | None = None) -> None:
+    args = build_parser().parse_args(argv)
+    dataframe = pd.read_csv(args.input)
+    output_dir = Path(args.output_dir)
+    sdf_dir = output_dir / "sdf"
+    output_dir.mkdir(parents=True, exist_ok=True)
+    sdf_dir.mkdir(parents=True, exist_ok=True)
+
+    position_counts = Counter(int(position) for position in dataframe["cleavage_position"])
+    stats = {
+        "position_counts": dict(sorted(position_counts.items())),
+        "total_fragments": int(len(dataframe)),
+        "total_parent_molecules": int(dataframe["parent_id"].nunique()),
+    }
+    (output_dir / "cleavage_position_statistics.json").write_text(
+        json.dumps(stats, indent=2, ensure_ascii=False) + "\n",
+        encoding="utf-8",
+    )
+
+    parent_rows = dataframe[["parent_id", "parent_smiles"]].drop_duplicates()
+    for parent in parent_rows.itertuples(index=False):
+        mol = Chem.MolFromSmiles(parent.parent_smiles)
+        if mol is None:
+            continue
+        mol = Chem.AddHs(mol)
+        params = AllChem.ETKDGv3()
+        if AllChem.EmbedMolecule(mol, params) != 0:
+            continue
+        AllChem.UFFOptimizeMolecule(mol)
+        writer = Chem.SDWriter(str(sdf_dir / f"{parent.parent_id}_3d.sdf"))
+        writer.write(mol)
+        writer.close()
 
 
 if __name__ == "__main__":