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--- |
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license: mit |
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tags: |
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- binding-affinity |
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- biology |
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- chemistry |
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pretty_name: Binding Affinity |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "affinity-data-combined.parquet" |
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- split: davis |
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path: "davis.parquet" |
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- split: davis_filtered |
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path: "davis-filtered.parquet" |
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- split: kiba |
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path: "kiba.parquet" |
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- split: pdbbind_2020_combined |
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path: "pdbbind-2020-combined.parquet" |
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- split: pdbbind_2020_refined |
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path: "pdbbind-2020-refined.parquet" |
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- split: bindingdb_ic50 |
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path: "bindingdb-ic50.parquet" |
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- split: bindingdb_ki |
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path: "bindingdb-ki.parquet" |
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- split: bindingdb_kd_filtered |
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path: "bindingdb-kd-filtered.parquet" |
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- split: bindingdb_kd |
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path: "bindingdb-kd.parquet" |
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- split: glaser |
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path: "glaser.parquet" |
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--- |
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# Binding Affinity Dataset |
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## Overview |
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This dataset is a comprehensive collection of protein-ligand binding affinity data, compiled from multiple sources. The dataset is structured with multiple splits, each corresponding to a specific source: |
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- BindingDB IC50 Split |
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- BindingDB Kd Split |
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- BindingDB Kd Filtered Split |
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- BindingDB Ki Split |
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- Davis Split |
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- Davis Filtered Split |
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- KIBA Split |
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- PDBBind 2020 Combined Split |
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- PDBBind 2020 Refined Split |
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- Glaser Split |
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In addition to these source-specific splits, a main training split is provided that combines and aggregates data from all these sources. |
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## Training Dataset Composition |
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The training split is a comprehensive aggregation of multiple molecular binding datasets: |
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- Davis-filtered dataset |
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- PDBBind 2020 Combined dataset |
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- BindingDB IC50 dataset |
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- BindingDB Ki dataset |
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- BindingDB Kd Filtered dataset |
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- Glaser dataset |
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## Preprocessing Steps |
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1. **Dataset Merging**: All specified datasets were combined into a single dataset. |
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2. **Duplicate Removal**: Duplicate entries were dropped to ensure data uniqueness. |
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3. **Binding Affinity Normalization**: |
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- Entries with a binding affinity of 5 were reduced |
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- For duplicate protein-ligand pairs, the mean binding affinity was calculated |
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## Data Sources |
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| Dataset | Source | Notes | |
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|---------|--------|-------| |
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| bindingdb_ic50.parquet | [TDC Python Package](https://tdcommons.ai/) | Therapeutic Data Commons | |
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| bindingdb_kd.parquet | [TDC Python Package](https://tdcommons.ai/) | Therapeutic Data Commons | |
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| bindingdb_kd_filtered.parquet | Manually Filtered | See `standardize_data.ipynb` | |
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| bindingdb_ki.parquet | [TDC Python Package](https://tdcommons.ai/) | Therapeutic Data Commons | |
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| davis.parquet | [TDC Python Package](https://tdcommons.ai/) | Therapeutic Data Commons | |
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| davis_filtered.parquet | [Kaggle Dataset](https://www.kaggle.com/datasets/christang0002/davis-and-kiba) | Filtered Davis dataset | |
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| kiba.parquet | [TDC Python Package](https://tdcommons.ai/) | Therapeutic Data Commons | |
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| pdbbind_2020_combined.parquet | [PDBBind](https://www.pdbbind.org.cn/) | Combined PDBBind 2020 dataset | |
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| pdbbind_2020_refined.parquet | [PDBBind](https://www.pdbbind.org.cn/) | Refined PDBBind 2020 dataset | |
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| glaser.parquet | [HuggingFace Dataset](https://huggingface.co/datasets/jglaser/binding_affinity) | Glaser binding affinity dataset | |
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## Dataset Columns |
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| Column | Description | |
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|--------|-------------| |
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| `seq` | Protein sequence | |
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| `smiles_can` | Canonical SMILES representation of the ligand | |
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| `affinity_uM` | Binding affinity in micromolar (µM) concentration | |
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| `neg_log10_affinityM` | Negative logarithm (base 10) of the affinity in molar concentration | |
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| `affinity_norm` | Normalized binding affinity | |
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| `affinity_mean` | Mean binding affinity for duplicate protein-ligand pairs | |
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| `affinity_std` | Standard deviation of binding affinity for duplicate protein-ligand pairs | |
