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binding-affinity-PL

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metadata
license: mit
tags:
  - binding-affinity
  - biology
  - chemistry
pretty_name: Binding Affinity
configs:
  - config_name: default
    data_files:
      - split: train
        path: affinity-data-combined.parquet
      - split: davis
        path: davis.parquet
      - split: davis_filtered
        path: davis-filtered.parquet
      - split: kiba
        path: kiba.parquet
      - split: pdbbind_2020_combined
        path: pdbbind-2020-combined.parquet
      - split: pdbbind_2020_refined
        path: pdbbind-2020-refined.parquet
      - split: bindingdb_ic50
        path: bindingdb-ic50.parquet
      - split: bindingdb_ki
        path: bindingdb-ki.parquet
      - split: bindingdb_kd_filtered
        path: bindingdb-kd-filtered.parquet
      - split: bindingdb_kd
        path: bindingdb-kd.parquet
      - split: glaser
        path: glaser.parquet

Binding Affinity Dataset

Overview

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:

  • BindingDB IC50 Split
  • BindingDB Kd Split
  • BindingDB Kd Filtered Split
  • BindingDB Ki Split
  • Davis Split
  • Davis Filtered Split
  • KIBA Split
  • PDBBind 2020 Combined Split
  • PDBBind 2020 Refined Split
  • Glaser Split

In addition to these source-specific splits, a main training split is provided that combines and aggregates data from all these sources.

Training Dataset Composition

The training split is a comprehensive aggregation of multiple molecular binding datasets:

  • Davis-filtered dataset
  • PDBBind 2020 Combined dataset
  • BindingDB IC50 dataset
  • BindingDB Ki dataset
  • BindingDB Kd Filtered dataset
  • Glaser dataset

Preprocessing Steps

  1. Dataset Merging: All specified datasets were combined into a single dataset.
  2. Duplicate Removal: Duplicate entries were dropped to ensure data uniqueness.
  3. Binding Affinity Normalization:
    • Entries with a binding affinity of 5 were reduced
    • For duplicate protein-ligand pairs, the mean binding affinity was calculated

Data Sources

Dataset Source Notes
bindingdb_ic50.parquet TDC Python Package Therapeutic Data Commons
bindingdb_kd.parquet TDC Python Package Therapeutic Data Commons
bindingdb_kd_filtered.parquet Manually Filtered See standardize_data.ipynb
bindingdb_ki.parquet TDC Python Package Therapeutic Data Commons
davis.parquet TDC Python Package Therapeutic Data Commons
davis_filtered.parquet Kaggle Dataset Filtered Davis dataset
kiba.parquet TDC Python Package Therapeutic Data Commons
pdbbind_2020_combined.parquet PDBBind Combined PDBBind 2020 dataset
pdbbind_2020_refined.parquet PDBBind Refined PDBBind 2020 dataset
glaser.parquet HuggingFace Dataset Glaser binding affinity dataset

Dataset Columns

Column Description
seq Protein sequence
smiles_can Canonical SMILES representation of the ligand
affinity_uM Binding affinity in micromolar (µM) concentration
neg_log10_affinityM Negative logarithm (base 10) of the affinity in molar concentration
affinity_norm Normalized binding affinity
affinity_mean Mean binding affinity for duplicate protein-ligand pairs
affinity_std Standard deviation of binding affinity for duplicate protein-ligand pairs