Update README.md

README.md

@@ -79,7 +79,9 @@ configs:
     path: IUPAC_pKa/test-*
 ---
 
-
+# IUPAC_pKa
+IUPAC_pKa
+This is a mirror of the [Official Github repo](https://github.com/IUPAC/Dissociation-Constants?tab=readme-ov-file) where the dataset v2_2 was uploaded.
 
 
 ## Quickstart Usage
@@ -94,29 +96,26 @@ then, from within python load the datasets library
 
     >>> import datasets
    
-and load one of the `AqSolDB` datasets, e.g.,
+and load the `IUPAC_pKa` datasets, e.g.,
 
-    >>> AqSolDB = datasets.load_dataset("maomlab/IUPAC_pKa", name = "IUPAC_pKa")
-    Downloading readme: 100%|████████████████████| 10.2k/10.2k [00:00<00:00, 4.41MB/s]
-    Downloading data: 100%|█████████████████████████| 972k/972k [00:02<00:00, 432kB/s]
-    Downloading data: 100%|██████████████████████| 2.88M/2.88M [00:01<00:00, 1.92MB/s]
-    Generating test split: 100%|████████| 2494/2494 [00:00<00:00, 44727.48 examples/s]
-    Generating train split: 100%|██████| 7488/7488 [00:00<00:00, 144316.82 examples/s]
+    >>> IUPAC_pKa = datasets.load_dataset('maomlab/IUPAC_pKa')
+    README.md: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5.06k/5.06k [00:00<00:00, 771kB/s]
+    train-00000-of-00001.parquet: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 947k/947k [00:00<00:00, 34.0MB/s]
+    test-00000-of-00001.parquet: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 519k/519k [00:00<00:00, 23.5MB/s]
+    Generating train split: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 18168/18168 [00:00<00:00, 260823.23 examples/s]
+    Generating test split: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 6054/6054 [00:00<00:00, 231724.00 examples/s]
 
 and inspecting the loaded dataset
 
-    >>> AqSolDB
-    AqSolDB
+    >>> IUPAC_pKa
     DatasetDict({
-        test: Dataset({
-            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
-    ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
-            num_rows: 2494
-        })
         train: Dataset({
-            features: ['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'Y', 'SD', 'Ocurrences', 'Group', 'MolWt', 'MolLogP', 'MolMR', 'HeavyAtomCount', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumValenceEl\
-    ectrons', 'NumAromaticRings', 'NumSaturatedRings', 'NumAliphaticRings', 'RingCount', 'TPSA', 'LabuteASA', 'BalabanJ', 'BertzCT', 'ClusterNo', 'MolCount', 'group'],
-            num_rows: 7488
+            features: ['unique_ID', 'SMILES', 'InChI', 'pka_type', 'Y', 'T', 'remarks', 'method', 'assessment', 'ref', 'ref_remarks', 'entry_remarks', 'original_IUPAC_names', 'name_contributors', 'num_name_contributors', 'original_IUPAC_nicknames', 'source', 'pressure', 'acidity_label', 'original_T', 'solvent', 'ClusterNo', 'MolCount', 'group'],
+            num_rows: 18168
+        })
+        test: Dataset({
+            features: ['unique_ID', 'SMILES', 'InChI', 'pka_type', 'Y', 'T', 'remarks', 'method', 'assessment', 'ref', 'ref_remarks', 'entry_remarks', 'original_IUPAC_names', 'name_contributors', 'num_name_contributors', 'original_IUPAC_nicknames', 'source', 'pressure', 'acidity_label', 'original_T', 'solvent', 'ClusterNo', 'MolCount', 'group'],
+            num_rows: 6054
         })
     })
 
@@ -136,7 +135,7 @@ then load, featurize, split, fit, and evaluate the catboost model
     from molflux.modelzoo import load_from_dict as load_model_from_dict
     from molflux.metrics import load_suite
     
-    split_dataset = load_dataset('maomlab/AqSolDB')
+    split_dataset = load_dataset('maomlab/IUPAC_pKa')
     
     split_featurised_dataset = featurise_dataset(
       split_dataset,
@@ -156,4 +155,9 @@ then load, featurize, split, fit, and evaluate the catboost model
     
     scores = regression_suite.compute(
         references=split_featurised_dataset["test"]['Y'],
-        predictions=preds["cat_boost_regressor::Y"])    
+        predictions=preds["cat_boost_regressor::Y"])
+
+
+### Citation
+https://zenodo.org/records/13987352
+