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@@ -32,14 +32,67 @@ configs:
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  path: "glaser.parquet"
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  ---
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  ## Data Sources
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- bindingdb_ic50.parquet: https://tdcommons.ai/ (tdc python package)
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- bindingdb_kd.parquet: https://tdcommons.ai/ (tdc python package)
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- bindingdb_kd_filtered.parquet: filtered manually (see standardize_data.ipynb)
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- bindingdb_ki.parquet: https://tdcommons.ai/ (tdc python package)
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- davis.parquet: https://tdcommons.ai/ (tdc python package)
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- davis_filtered.parquet: https://www.kaggle.com/datasets/christang0002/davis-and-kiba
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- kiba.parquet: https://tdcommons.ai/ (tdc python package)
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- pdbbind_2020_combined.parquet: https://www.pdbbind.org.cn/
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- pdbbind_2020_refined.parquet: https://www.pdbbind.org.cn/
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- glaser.parquet: https://huggingface.co/datasets/jglaser/binding_affinity
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  path: "glaser.parquet"
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  ---
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+ # Binding Affinity Dataset
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+
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+ ## Overview
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+
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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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+
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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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+
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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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+
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+ ## Training Dataset Composition
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+
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+ The training split is a comprehensive aggregation of multiple molecular binding datasets:
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+
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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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+
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+ ## Preprocessing Steps
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+
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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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+
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  ## Data Sources
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+
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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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+
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+ ## Dataset Columns
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+
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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 |