---
language:
- en
pretty_name: Bioactivity Report QM9 - Molecular Data Preprocessing and ML Pipeline
size_categories:
- 100K<n<1M
---
# Bioactivity Report QM9 - Molecular Data Preprocessing and ML Pipeline


This data set provides a comprehensive set of quantum chemical properties for a relevant and consistent chemical space of small organic molecules. 
The dataset consists of computed geometric, energetic, electronic, and thermodynamic properties for 134k stable small organic molecules made up of CHONF, corresponding to the subset of all 133,885 species with up to nine heavy atoms (CONF) out of the GDB-17 chemical universe the properties were calculated at the B3LYP/6-31G(2df,p) level of quantum chemistry. 

## Format

| Property Group (Column Range)                         | Columns                                                                                                                                                             |
|--------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Identifier (1–1)                                       | `ID`                                                                                                                                                                 |
| Chemical descriptors (2–9)                             | `MolWt`, `ClogP`, `TPSA`, `HBD`, `HBA`, `NumRotatableBonds`, `RingCount`, `FractionCSP3`                                                                             |
| Quantum-chemical & thermodynamic properties (10–24)    | `Rotational_Constant_A`, `Rotational_Constant_B`, `Rotational_Constant_C`, `Dipole_Moment`, `Isotropic_polarizability`, `Energy_of_HOMO`, `Energy_of_LUMO`, `LUMO_HOMO_GAP`, `Electronic_spatial_extent`, `Zero_point_vibrational_energy`, `Internal_energy_at_0_K`, `Internal_energy_at_298.15_K`, `Enthalpy_at_298.15_K`, `Free_energy_at_298.15_K`, `Heat_capacity_at_298.15_K` |
| Structural fingerprint (25–25)                         | `Ecfp_4` (2,048-bit Morgan fingerprint, radius 2)                                                                 

## Sources: 

    L. Ruddigkeit, R. van Deursen, L. C. Blum, J.-L. Reymond, Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17, J. Chem. Inf. Model. 52, 2864–2875, 2012.
    R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld, Quantum chemistry structures and properties of 134 kilo molecules, Scientific Data 1, 140022, 2014. 

## Data processing

This pipeline transforms raw .xyz molecular files into a structured ML-ready format by:
    1. Extracting properties
    2. Calculating ECFP4 fingerprints
    3. Cleaning and filtering problematic entries
    4. Organizing columns and unpacking features
    5. Finalizing the dataset for ML
    6. Splitting it into training, validation, and test sets

### Scripts

| File                     | Description |
|--------------------------|-------------|
| `00_download_data.sh`    | Downloads, unzips, and moves the files into a directory called `input_data`. |
| `01_batch_data.py`       | Creates many batch files, each containing a portion of the data as file paths. Batching helps parallelize the next step. |
| `02_process_batch.py`    | Reads and parses `.xyz` files, saving processed molecular data (e.g., atoms, coordinates, properties) in `.parquet` format. |
| `03_run_batches.sh`      | A shell script to process batches of `.xyz` files using SLURM or local runs. Calls `02_process_batch.py` over all batch files. |
| `04_merge_data.py`       | Merges all batch-level `.parquet` outputs into a single unified dataset. |
| `05_sanitize_data.py`    | Standardizes SMILES strings and removes molecules that fail sanitization. |
| `06_calculate_ecfp.py`   | Computes ECFP4 molecular fingerprints using RDKit. |
| `07_calculate_properties.py` | Calculates and appends molecular properties to the dataset. |
| `08_organize_columns.py` | Reorganizes column order, drops unnecessary columns, and prepares a clean structure. |
| `09_unpack_ecfp4.py`     | Unpacks the ECFP4 bit vectors into individual binary columns for ML input. |
| `10_finalize_ml_data.py` | Final checks and formatting to ensure the data is ready for ML training. |
| `11_split_ml_data.py`    | Splits the final dataset into train/val/test sets using fixed proportions (e.g., 80/10/10).


# License

This project is licensed under CC0 (for use with QM9 which is public domain). Attribution is appreciated but not required.