Handle compound_name is not given in metadata.

CHANGELOG.md

@@ -5,6 +5,10 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## 1.5.2
+### Fixed
+- Handle missing compound names. Previously MS2Query would break, when no compound name was available for an analog.
+
 ## 1.5.1
 ### Fixed
 - Set max matchms version, since breaking change was introduced (missing add_losses)

ms2query/results_table.py

@@ -127,18 +127,26 @@ def export_to_dataframe(
         selected_analogs = selected_analogs[
             (selected_analogs["ms2query_model_prediction"] > minimal_ms2query_score)]
         nr_of_analogs = len(selected_analogs)
-        # Return None if know analogs are selected.
+        # Return None if no analogs are selected.
         if selected_analogs.empty:
             return None
 
         # For each analog the compound name is selected from sqlite
         metadata_dict = self.sqlite_library.get_metadata_from_sqlite(list(selected_analogs["spectrum_ids"]))
-        compound_name_list = [metadata_dict[analog_spectrum_id]["compound_name"]
-                              for analog_spectrum_id
-                              in list(selected_analogs["spectrum_ids"])]
-        smiles_list = [metadata_dict[analog_spectrum_id]["smiles"]
-                       for analog_spectrum_id
-                       in list(selected_analogs["spectrum_ids"])]
+
+        compound_name_list = []
+        for metadata in metadata_dict.values():
+            if "compound_name" in metadata.keys():
+                compound_name_list.append(metadata["compound_name"])
+            else:
+                compound_name_list.append(None)
+
+        smiles_list = []
+        for metadata in metadata_dict.values():
+            if "smiles" in metadata.keys():
+                smiles_list.append(metadata["smiles"])
+            else:
+                smiles_list.append(None)
 
         # Add inchikey and ms2query model prediction to results df
         # results_df = selected_analogs.loc[:, ["spectrum_ids", "ms2query_model_prediction", "inchikey"]]