Removed invalid SMILES

.gitattributes

@@ -0,0 +1 @@
+*.parquet filter=lfs diff=lfs merge=lfs -text

FSMol/{test-00000-of-00001.parquet → FSMol_test.parquet}

@@ -1,3 +1,3 @@
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-size 1535970
+oid sha256:a0273f3d3672e4825d73561e705ad64a6776291a1c7c928494d83315ff5cce20
+size 1023125

FSMol/{train-00000-of-00001.parquet → FSMol_train.parquet}

@@ -1,3 +1,3 @@
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+size 127780673

FSMol/{validation-00000-of-00001.parquet → FSMol_valid.parquet}

@@ -1,3 +1,3 @@
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-oid sha256:e63ffaa6a99b221e4b1f0c7e640f5980b7ac5d61a8399ea5d8921f9776d3b5aa
-size 517292
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+size 428531

README.md

@@ -67,6 +67,12 @@ This is a mirror of the [Official Github repo](https://github.com/microsoft/FS-M
 
 
 ## Preprocessing
+[Update 2025.08.06 Version 1.1.0]
+We removed invalid SMILES strings from the dataset, which could not be parsed by RDKit.
+- Train split: removed 12470 strings from 5038727 strings
+- Test split: removed 127 strings from 56220 strings
+- Validation split: removed 18 strings from 19008 strings 
+
 
 We utilized the raw data uploaded on [Github](https://github.com/microsoft/FS-Mol) and performed several preprocessing:
 1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
@@ -105,15 +111,15 @@ and inspecting the loaded dataset
     DatasetDict({
             train: Dataset({
                 features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
-                num_rows: 5038727
+                num_rows: 5026257
             })
             test: Dataset({
                 features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
-                num_rows: 56220
+                num_rows: 56093
             })
             validation: Dataset({
                 features: ['SMILES', 'Y', 'Assay_ID', 'RegressionProperty', 'LogRegressionProperty', 'Relation'],
-                num_rows: 19008
+                num_rows: 18990
             })
 
     })