Duplicate from yairschiff/qm9

Co-authored-by: Yair Schiff <yairschiff@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: num_atoms
+    dtype: int64
+  - name: atomic_symbols
+    sequence: string
+  - name: pos
+    sequence:
+      sequence: float64
+  - name: charges
+    sequence: float64
+  - name: harmonic_oscillator_frequencies
+    sequence: float64
+  - name: smiles
+    dtype: string
+  - name: inchi
+    dtype: string
+  - name: A
+    dtype: float64
+  - name: B
+    dtype: float64
+  - name: C
+    dtype: float64
+  - name: mu
+    dtype: float64
+  - name: alpha
+    dtype: float64
+  - name: homo
+    dtype: float64
+  - name: lumo
+    dtype: float64
+  - name: gap
+    dtype: float64
+  - name: r2
+    dtype: float64
+  - name: zpve
+    dtype: float64
+  - name: u0
+    dtype: float64
+  - name: u
+    dtype: float64
+  - name: h
+    dtype: float64
+  - name: g
+    dtype: float64
+  - name: cv
+    dtype: float64
+  - name: canonical_smiles
+    dtype: string
+  - name: logP
+    dtype: float64
+  - name: qed
+    dtype: float64
+  - name: np_score
+    dtype: float64
+  - name: sa_score
+    dtype: float64
+  - name: ring_count
+    dtype: int64
+  - name: R3
+    dtype: int64
+  - name: R4
+    dtype: int64
+  - name: R5
+    dtype: int64
+  - name: R6
+    dtype: int64
+  - name: R7
+    dtype: int64
+  - name: R8
+    dtype: int64
+  - name: R9
+    dtype: int64
+  - name: single_bond
+    dtype: int64
+  - name: double_bond
+    dtype: int64
+  - name: triple_bond
+    dtype: int64
+  - name: aromatic_bond
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 199395693
+    num_examples: 133885
+  download_size: 180380355
+  dataset_size: 199395693
+---
+# Dataset Card for "QM9"
+
+QM9 dataset from [Ruddigkeit et al., 2012](https://pubs.acs.org/doi/full/10.1021/ci300415d);
+[Ramakrishnan et al., 2014](https://www.nature.com/articles/sdata201422).
+
+Original data downloaded from: http://quantum-machine.org/datasets.
+Additional annotations (QED, logP, SA score, NP score, bond and ring counts) added using [`rdkit`](https://www.rdkit.org/docs/index.html) library.
+
+## Quick start usage:
+```python
+from datasets import load_dataset
+
+ds = load_dataset("yairschiff/qm9")
+
+# Random train/test splits as recommended by:
+#   https://moleculenet.org/datasets-1
+test_size = 0.1
+seed = 1
+ds.train_test_split(test_size=test_size, seed=seed)
+
+# Use `ds['canonical_smiles']` from `rdkit` as inputs.
+```
+
+
+## Full processing steps
+
+```python
+import os
+import typing
+
+import datasets
+import numpy as np
+import pandas as pd
+import rdkit
+import torch
+from rdkit import Chem as rdChem
+from rdkit.Chem import Crippen, QED
+from rdkit.Contrib.NP_Score import npscorer
+from rdkit.Contrib.SA_Score import sascorer
+from tqdm.auto import tqdm
+
+# TODO: Update to 2024.03.6 release when available instead of suppressing warning!
+#  See: https://github.com/rdkit/rdkit/issues/7625#
+rdkit.rdBase.DisableLog('rdApp.warning')
+
+def parse_float(
+    s: str
+) -> float:
+    """Parses floats potentially written as exponentiated values.
+    
+        Copied from https://www.kaggle.com/code/tawe141/extracting-data-from-qm9-xyz-files/code
+    """
+    try:
+        return float(s)
+    except ValueError:
+        base, power = s.split('*^')
+        return float(base) * 10**float(power)
+
+
+def count_rings_and_bonds(
+    mol: rdChem.Mol, max_ring_size: int = -1
+) -> typing.Dict[str, int]:
+    """Counts bond and ring (by type)."""
+    
+    # Counting rings
+    ssr = rdChem.GetSymmSSSR(mol)
+    ring_count = len(ssr)
+    
+    ring_sizes = {} if max_ring_size < 0 else {i: 0 for i in range(3, max_ring_size+1)}
+    for ring in ssr:
+        ring_size = len(ring)
+        if ring_size not in ring_sizes:
+            ring_sizes[ring_size] = 0
+        ring_sizes[ring_size] += 1
+    
+    # Counting bond types
+    bond_counts = {
+        'single': 0,
+        'double': 0,
+        'triple': 0,
+        'aromatic': 0
+    }
+    
+    for bond in mol.GetBonds():
+        if bond.GetIsAromatic():
+            bond_counts['aromatic'] += 1
+        elif bond.GetBondType() == rdChem.BondType.SINGLE:
+            bond_counts['single'] += 1
+        elif bond.GetBondType() == rdChem.BondType.DOUBLE:
+            bond_counts['double'] += 1
+        elif bond.GetBondType() == rdChem.BondType.TRIPLE:
+            bond_counts['triple'] += 1
+    result = {
+        'ring_count': ring_count,
+    }
+    for k, v in ring_sizes.items():
+        result[f"R{k}"] = v
+
+    for k, v in bond_counts.items():
+        result[f"{k}_bond"] = v
+    return result
+
+
+def parse_xyz(
+    filename: str,
+    max_ring_size: int = -1,
+    npscorer_model: typing.Optional[dict] = None,
+    array_format: str = 'np'
+) -> typing.Dict[str, typing.Any]:
+    """Parses QM9 specific xyz files. 
+    
+        See https://www.nature.com/articles/sdata201422/tables/2 for reference.
+        Adapted from https://www.kaggle.com/code/tawe141/extracting-data-from-qm9-xyz-files/code
+    """
+    assert array_format in ['np', 'pt'], \
+        f"Invalid array_format: `{array_format}` provided. Must be one of `np` (numpy.array), `pt` (torch.tensor)."
+    
+    num_atoms = 0
+    scalar_properties = []
+    atomic_symbols = []
+    xyz = []
+    charges = []
+    harmonic_vibrational_frequencies = []
+    smiles = ''
+    inchi = ''
+    with open(filename, 'r') as f:
+        for line_num, line in enumerate(f):
+            if line_num == 0:
+                num_atoms = int(line)
+            elif line_num == 1:
+                scalar_properties = [float(i) for i in line.split()[2:]]
+            elif 2 <= line_num <= 1 + num_atoms:
+                atom_symbol, x, y, z, charge = line.split()
+                atomic_symbols.append(atom_symbol)
+                xyz.append([parse_float(x), parse_float(y), parse_float(z)])
+                charges.append(parse_float(charge))
+            elif line_num == num_atoms + 2:
+                harmonic_vibrational_frequencies = [float(i) for i in line.split()]
+            elif line_num == num_atoms + 3:
+                smiles = line.split()[0]
+            elif line_num == num_atoms + 4:
+                inchi = line.split()[0]
+
+    array_wrap = np.array if array_format == 'np' else torch.tensor
+    result = {
+        'num_atoms': num_atoms,
+        'atomic_symbols': atomic_symbols,
+        'pos': array_wrap(xyz),
+        'charges': array_wrap(charges),
+        'harmonic_oscillator_frequencies': array_wrap(harmonic_vibrational_frequencies),
+        'smiles': smiles,
+        'inchi': inchi
+    }
+    scalar_property_labels = [
+        'A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'u0', 'u', 'h', 'g', 'cv'
+    ]    
+    scalar_properties = dict(zip(scalar_property_labels, scalar_properties))
+    result.update(scalar_properties)
+
+    # RdKit
+    result['canonical_smiles'] = rdChem.CanonSmiles(result['smiles'])
+    m = rdChem.MolFromSmiles(result['canonical_smiles'])
+    result['logP'] = Crippen.MolLogP(m)
+    result['qed'] = QED.qed(m)
+    if npscorer_model is not None:
+        result['np_score'] = npscorer.scoreMol(m, npscorer_model)
+    result['sa_score'] = sascorer.calculateScore(m)
+    result.update(count_rings_and_bonds(m, max_ring_size=max_ring_size))
+    
+    return result
+
+"""
+    Download xyz files from:
+        https://figshare.com/collections/Quantum_chemistry_structures_and_properties_of_134_kilo_molecules/978904
+    > wget https://figshare.com/ndownloader/files/3195389/dsgdb9nsd.xyz.tar.bz2
+    > mkdir dsgdb9nsd.xyz
+    > tar -xvjf dsgdb9nsd.xyz.tar.bz2 -C dsgdb9nsd.xyz
+"""
+MAX_RING_SIZE = 9
+fscore = npscorer.readNPModel()
+xyz_dir_path = '<PATH TO dsgdb9nsd.xyz>'
+parsed_xyz = []
+for file in tqdm(sorted(os.listdir(xyz_dir_path)), desc='Parsing'):
+    parsed = parse_xyz(os.path.join(xyz_dir_path, file),
+                       max_ring_size=MAX_RING_SIZE,
+                       npscorer_model=fscore,
+                       array_format='np')
+    parsed_xyz.append(parsed)
+
+qm9_df = pd.DataFrame(data=parsed_xyz)
+
+# Conversion below is needed to avoid:
+#   `ArrowInvalid: ('Can only convert 1-dimensional array values',
+#   'Conversion failed for column pos with type object')`
+qm9_df['pos'] = qm9_df['pos'].apply(lambda x: [xi for xi in x])
+
+dataset = datasets.Dataset.from_pandas(qm9_df)
+```

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