| --- |
| language: |
| - en |
| tags: |
| - biology |
| - chemistry |
| - protein-ligand-binding |
| - drug-discovery |
| - molecular-modeling |
| - structural-biology |
| size_categories: |
| - 1K<n<10K |
| license: mit |
| --- |
| |
|
|
| # Overview |
| The LBA (Ligand Binding Affinity) dataset is designed for predicting the binding affinity between ligands and proteins based on co-crystallized protein-ligand complex structures. This dataset enables machine learning models to learn structure-activity relationships for drug discovery applications. |
|
|
| **Task**: Regression - predict experimentally measured binding affinity as pK (defined as -log(Ki) or -log(Kd)) |
|
|
| **Size**: 4,463 protein-ligand complexes |
|
|
|
|
| ## LBA: Ligand Binding Affinity Dataset - Source Descirption |
| In this task, we predict the binding affinity of ligands to their corresponding proteins based on the co-crystallized structure of the protein-ligand complex. We predict experimentally measured binding affinity as pK, defined as -log(Ki) or -log(Kd), depending on which measurement is available. |
|
|
| We derive crystal structures and ligand binding data from PDBBind (Wang et al., 2004), a widely-used curated database of protein-ligand complexes with experimental affinities derived from literature. We use the 2019 update of the so-called "refined set", a subset of complexes selected based on the quality of the structures and the affinity data. After filtering ligands which could not be read by RDKit due to invalid bonding data, |
| our final dataset consists of 4,463 complexes. |
|
|
| ## Dataset Structure |
|
|
| The dataset is organized with the following directory structure: |
|
|
| ``` |
| dataset/ |
| ├── data/ # Processed .pt files |
| │ ├── {pdb_id}_protein.pt # Full protein structure |
| │ ├── {pdb_id}_pocket.pt # Binding pocket (within 6Å of ligand) |
| │ └── {pdb_id}_ligand.pt # Ligand structure with bonds |
| ├── summary.csv # Dataset metadata and file mappings |
| ├── seq-id-30.json # 30% sequence identity splits |
| └── seq-id-60.json # 60% sequence identity splits |
| ``` |
|
|
| ### Data Splits |
|
|
| Two splitting strategies are provided to prevent data leakage: |
|
|
| - **seq-id-30**: No proteins with >30% sequence identity in the same split |
| - **seq-id-60**: No proteins with >60% sequence identity in the same split |
|
|
| Each split contains: |
| - `train`: Training set |
| - `val`: Validation set |
| - `test`: Test set |
|
|
| ## Data Fields |
|
|
| ### Protein/Pocke/Ligand Files |
| Each protein/pocket `.pt` file contains: |
| - `pos` (torch.Tensor): 3D atomic coordinates [N, 3] |
| - `z` (torch.Tensor): Atomic numbers [N] |
| - `res_name` (list): Three-letter amino acid codes |
| - `res_idx` (torch.Tensor): Residue indices [N] |
| - `bfactor` (torch.Tensor): B-factor values (thermal motion) [N] |
| - `is_alpha_carbon` (torch.Tensor): Boolean mask for CA atoms [N] |
| - `chain` (list): Chain identifiers |
| - `pdb_id` (str): PDB identifier |
|
|
| ### Ligand Files |
| Each ligand `.pt` file contains all protein/pocket fields plus: |
| - `edge_index_1d` (torch.Tensor): Bond connectivity [2, E] |
| - `edge_attr` (torch.Tensor): Bond types (1.0=single, 2.0=double, 3.0=triple, 1.5=aromatic) |
| - `smiles` (str): SMILES representation |
| - `neglog_aff` (float): Target binding affinity (-log(Ki/Kd)) |
|
|
| ## Usage |
|
|
| ### Loading with the Dataset Class |
|
|
| ```python |
| from lba_dataset import LBADataset_base |
| |
| # Load dataset |
| dataset = LBADataset_base( |
| split='train', |
| task_name='seq-id-30', |
| output=['pocket', 'ligand', 'neglog_aff'] |
| ) |
| |
| # Access samples |
| pocket, ligand, affinity = dataset[0] |
| print(f"Pocket atoms: {pocket['pos'].shape[0]}") |
| print(f"Ligand atoms: {ligand['pos'].shape[0]}") |
| print(f"Binding affinity: {affinity}") |
| ``` |
|
|
| ### Available Outputs |
| Configure what data to load with the `output` parameter: |
| - `'protein'`: Full protein structure (large files) |
| - `'pocket'`: Binding pocket only (recommended) |
| - `'ligand'`: Ligand structure with bonds |
| - `'neglog_aff'`: Binding affinity target |
| - `'smiles'`: Ligand SMILES string |
|
|
|
|
| ## Dataset Statistics |
|
|
| - **Total complexes**: 4,463 |
| - **Binding affinity range**: Varies (pK values) |
| - **Structure quality**: High (PDBBind refined set) |
| - **Ligand diversity**: Filtered for RDKit compatibility |
| - **Average pocket size**: ~150-300 atoms |
| - **Average ligand size**: ~20-50 atoms |
|
|
| ## Data Processing |
| For the original raw data and processing scripts, see the source repository here. |
|
|
| https://zenodo.org/records/4914718 |
