--- 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 = '' 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) ```