Removed invalid SMILES and updated README.md

README.md

@@ -1,4 +1,43 @@
 ---
+version: 1.1.1
+language: en
+license: gpl-3.0
+size_categories:
+- 1M<n<10M
+task_categories:
+- tabular-regression
+pretty_name: Molecule3D
+tags:
+- molecular geometry
+- molecular graph
+dataset_summary: Curated dataset of ground-state geometries of 4 million molecules
+  dervied from density functional theory, consisting of SMILES, sdf, and 3D properties
+  of molecules.  Random split and scaffold split datasets are uploaded to our repository.
+citation: '@misc{https://doi.org/10.48550/arxiv.2110.01717, doi = {10.48550/ARXIV.2110.01717},
+  url = {https://arxiv.org/abs/2110.01717}, author = {Xu,  Zhao and Luo,  Youzhi and
+  Zhang,  Xuan and Xu,  Xinyi and Xie,  Yaochen and Liu,  Meng and Dickerson,  Kaleb
+  and Deng,  Cheng and Nakata,  Maho and Ji,  Shuiwang}, keywords = {Machine Learning
+  (cs.LG),  Artificial Intelligence (cs.AI),  FOS: Computer and information sciences,  FOS:
+  Computer and information sciences}, title = {Molecule3D: A Benchmark for Predicting
+  3D Geometries from Molecular Graphs}, publisher = {arXiv}, year = {2021}, copyright
+  = {arXiv.org perpetual,  non-exclusive license} }'
+configs:
+- config_name: Molecule3D_random_split
+  data_files:
+  - split: train
+    path: Molecule3D_random_split/train-*
+  - split: test
+    path: Molecule3D_random_split/test-*
+  - split: validation
+    path: Molecule3D_random_split/validation-*
+- config_name: Molecule3D_scaffold_split
+  data_files:
+  - split: train
+    path: Molecule3D_scaffold_split/train-*
+  - split: validation
+    path: Molecule3D_scaffold_split/validation-*
+  - split: test
+    path: Molecule3D_scaffold_split/test-*
 dataset_info:
 - config_name: Molecule3D_random_split
   features:
@@ -25,15 +64,15 @@ dataset_info:
   - name: scf energy
     dtype: float64
   splits:
-  - name: train
-    num_bytes: 3175756092
-    num_examples: 2339728
-  - name: test
-    num_bytes: 1058783091
-    num_examples: 779895
-  - name: validation
-    num_bytes: 1058496510
-    num_examples: 779903
+    - name: train
+      num_bytes: 3175756092
+      num_examples: 2339728
+    - name: test
+      num_bytes: 1058783091
+      num_examples: 779895
+    - name: validation
+      num_bytes: 1058496510
+      num_examples: 779903
   download_size: 1881829100
   dataset_size: 5293035693
 - config_name: Molecule3D_scaffold_split
@@ -72,21 +111,123 @@ dataset_info:
     num_examples: 779859
   download_size: 1867676945
   dataset_size: 5293035693
-configs:
-- config_name: Molecule3D_random_split
-  data_files:
-  - split: train
-    path: Molecule3D_random_split/train-*
-  - split: test
-    path: Molecule3D_random_split/test-*
-  - split: validation
-    path: Molecule3D_random_split/validation-*
-- config_name: Molecule3D_scaffold_split
-  data_files:
-  - split: train
-    path: Molecule3D_scaffold_split/train-*
-  - split: validation
-    path: Molecule3D_scaffold_split/validation-*
-  - split: test
-    path: Molecule3D_scaffold_split/test-*
 ---
+
+# Molecule3D
+[Molecule3D](https://arxiv.org/abs/2110.01717) is a comprehensive dataset containing ground-state geometries derived from Density Functional Theory (DFT) calculations for approximately 4 million molecules. 
+This is a mirror of the [Official Github repo](https://github.com/divelab/MoleculeX/tree/molx/Molecule3D) where the dataset was uploaded in 2021.
+
+
+## Preprocseeing
+[Update on 2025.08.16 - version 1.1.1]
+We removed invalid SMILES strings which could not be parsed by RDKit.
+- Random split
+1. train : removed 60 smiles strings from 2339788 strings
+2. test : removed 35 smiles strings from 779930 strings
+3. validation : removed 26 smiles strings from 779929 strings
+- Scaffold split
+1. train : removed 46 smiles strings from 2339788 strings
+2. test : removed 71 smiles strings from 779930 strings
+3. validation : removed 4 smiles strings from 779929 strings
+
+We also updated the `README.md` file.
+
+We utilized the raw data uploaded on [Github](https://github.com/divelab/MoleculeX/tree/molx/Molecule3D/data/raw) and performed several preprocessing:
+1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
+2. Combine the SMILES strings, SDF data, and 3D molecular properties for each molecule.
+3. Split the dataset using random split and scaffold split (train, test, validation)
+
+If you would like to try these processes with the original dataset, 
+please follow the instructions in the [Preprocessing Script](https://huggingface.co/datasets/maomlab/Molecule3D/blob/main/Molecule3D_preprocessing.py) file located in our Molecule3D repository.
+
+
+
+## Quickstart Usage
+
+### Load a dataset in python
+Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
+First, from the command line install the `datasets` library
+
+    $ pip install datasets
+
+then, from within python load the datasets library
+
+    >>> import datasets
+   
+and load one of the `Molecule3D` datasets, e.g.,
+
+    >>> Molecule3D = datasets.load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split')   # can put 'Molecule3D_scaffold_split' for the name as well
+    README.md: 100%  4.95k/4.95k [00:00<00:00, 559kB/s]
+    Generating train split: 100%  2339788/2339788 [00:34<00:00, 85817.85 examples/s]
+    Generating test split: 100%  779930/779930 [00:15<00:00, 96660.33 examples/s]
+    Generating validation split:  100% 779929/779929 [00:09<00:00, 79064.99 examples/s]
+
+and inspecting the dataset
+
+    >>> Molecule3D
+    DatasetDict({
+    train: Dataset({
+        features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
+        num_rows: 2339788
+    })
+    test: Dataset({
+        features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
+        num_rows: 779930
+    })
+    validation: Dataset({
+        features: ['index', 'SMILES', 'sdf', 'cid', 'dipole x', 'dipole y', 'dipole z', 'homo', 'lumo', 'Y', 'scf energy'],
+        num_rows: 779929
+    })
+}) 
+
+
+### Use a dataset to train a model
+One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
+First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
+
+    pip install 'molflux[catboost,rdkit]'
+
+then load, featurize, split, fit, and evaluate the catboost model
+
+    import json
+    from datasets import load_dataset
+    from molflux.datasets import featurise_dataset
+    from molflux.features import load_from_dicts as load_representations_from_dicts
+    from molflux.splits import load_from_dict as load_split_from_dict
+    from molflux.modelzoo import load_from_dict as load_model_from_dict
+    from molflux.metrics import load_suite
+    
+    split_dataset = load_dataset('maomlab/Molecule3D', name = 'Molecule3D_random_split')  # can put 'Molecule3D_scaffold_split' for the name as well
+    
+    split_featurised_dataset = featurise_dataset(
+      split_dataset,
+      column = "SMILES",
+      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))
+
+    model = load_model_from_dict({
+        "name": "cat_boost_regressor",
+        "config": {
+            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
+            "y_features": ['Y']}})
+    
+    model.train(split_featurised_dataset["train"])
+    preds = model.predict(split_featurised_dataset["test"])
+    
+    regression_suite = load_suite("regression")
+    
+    scores = regression_suite.compute(
+        references=split_featurised_dataset["test"]['Y'],
+        predictions=preds["cat_boost_regressor::Y"])    
+
+
+## Citation
+@misc{https://doi.org/10.48550/arxiv.2110.01717,
+  doi = {10.48550/ARXIV.2110.01717},
+  url = {https://arxiv.org/abs/2110.01717},
+  author = {Xu,  Zhao and Luo,  Youzhi and Zhang,  Xuan and Xu,  Xinyi and Xie,  Yaochen and Liu,  Meng and Dickerson,  Kaleb and Deng,  Cheng and Nakata,  Maho and Ji,  Shuiwang},
+  keywords = {Machine Learning (cs.LG),  Artificial Intelligence (cs.AI),  FOS: Computer and information sciences,  FOS: Computer and information sciences},
+  title = {Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs},
+  publisher = {arXiv},
+  year = {2021},
+  copyright = {arXiv.org perpetual,  non-exclusive license}
+}