--- dataset_info: - config_name: BindingDB_filtered features: - name: Index dtype: string - name: Drug_ID dtype: string - name: Drug dtype: string - name: Target_ID dtype: string - name: Target dtype: string - name: Y dtype: float32 splits: - name: train num_examples: 24700 - config_name: CATS features: - name: Index dtype: string - name: Drug dtype: string - name: IC50 dtype: float32 - name: Target dtype: string - name: Y dtype: float32 splits: - name: train num_examples: 393 - config_name: HIF2A features: - name: Index dtype: string - name: Y dtype: float32 - name: Drug dtype: string - name: Target dtype: string splits: - name: train num_examples: 37 - config_name: HSP90 features: - name: Index dtype: string - name: Drug dtype: string - name: IC50 (nM) dtype: float32 - name: Target dtype: string - name: Y dtype: float32 splits: - name: train num_examples: 147 - config_name: LeakyPDB features: - name: Index dtype: string - name: header dtype: string - name: smiles dtype: string - name: category dtype: string - name: seq dtype: string - name: resolution dtype: float32 - name: date dtype: string - name: type dtype: string - name: new_split dtype: string - name: CL1 dtype: bool - name: CL2 dtype: bool - name: CL3 dtype: bool - name: remove_for_balancing_val dtype: bool - name: kd/ki dtype: string - name: value dtype: float32 - name: covalent dtype: bool splits: - name: train num_examples: 19443 - config_name: MCL1 features: - name: Index dtype: string - name: Y dtype: float32 - name: Drug dtype: string - name: Target dtype: string splits: - name: train num_examples: 25 - config_name: Mpro features: - name: Index dtype: string - name: Drug dtype: string - name: Y dtype: float32 - name: Target dtype: string splits: - name: train num_examples: 2062 - config_name: SYK features: - name: Index dtype: string - name: Y dtype: float32 - name: Drug dtype: string - name: Target dtype: string splits: - name: train num_examples: 44 configs: - config_name: BindingDB_filtered data_files: - split: train path: BindingDB_filtered/train/data-* - config_name: CATS data_files: - split: train path: CATS/train/data-* - config_name: HIF2A data_files: - split: train path: HIF2A/train/data-* - config_name: HSP90 data_files: - split: train path: HSP90/train/data-* - config_name: LeakyPDB data_files: - split: train path: LeakyPDB/train/data-* - config_name: MCL1 data_files: - split: train path: MCL1/train/data-* - config_name: Mpro data_files: - split: train path: Mpro/train/data-* - config_name: SYK data_files: - split: train path: SYK/train/data-* license: cc-by-4.0 pretty_name: BALM-Benchmark tags: - chemistry - biology size_categories: - 10K BALM-Benchmark is a comprehensive benchmark suite that combines multiple seminal binding affinity prediction datasets in one place. ... ## Dataset Details ### Dataset Description - **Dataset Repository:** https://huggingface.co/datasets/BALM/BALM-benchmark - **Code Repository:** https://github.com/meyresearch/BALM - **Paper:** TBA - **Language(s) (NLP):** English - **License:** CC-BY-4.0 ### Dataset Columns - **BindingDB_filtered**: - **Index** (`string`): Index of the ligand-target pair. - **Drug_ID** (`string`): Index of the ligand from the TDC. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **Target_ID** (`string`): Index of the target protein from the TDC. - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **Y** (`float32`): binding affinity value in pKd. - **CATS**: - **Index** (`string`): Index of the ligand-target pair. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **IC50** (`float32`): binding affinity value in IC50. - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **Y** (`float32`): binding affinity value in pKd. - **HIF2A**: - **Index** (`string`): Index of the ligand-target pair. - **Y** (`float32`): binding affinity value in pKd. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **HSP90**: - **Index** (`string`): Index of the ligand-target pair. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **IC50 (nM)** (`float32`): binding affinity value in IC50. - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **Y** (`float32`): binding affinity value in pKd. - **LeakyPDB**: - **Index** (`string`): Index of the ligand-target pair. - **header** (`string`): TBA - **smiles** (`string`): TBA - **category** (`string`): TBA - **seq** (`string`): TBA - **resolution** (`float32`): TBA - **date** (`string`): TBA - **type** (`string`): TBA - **new_split** (`string`): TBA - **CL1** (`bool`): TBA - **CL2** (`bool`): TBA - **CL3** (`bool`): TBA - **remove_for_balancing_val** (`bool`): TBA - **kd/ki** (`string`): TBA - **value** (`float32`): TBA - **covalent** (`bool`): TBA - **MCL1**: - **Index** (`string`): Index of the ligand-target pair. - **Y** (`float32`): binding affinity value in pKd. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **Mpro**: - **Index** (`string`): Index of the ligand-target pair. - **Y** (`float32`): binding affinity value in pKd. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). - **SYK**: - **Index** (`string`): Index of the ligand-target pair. - **Y** (`float32`): binding affinity value in pKd. - **Drug** (`string`): Ligand sequence (i.e., SMILES string). - **Target** (`string`): Protein sequence (i.e., sequence of amino acids). ### Dataset Sources - **BindingDB_filtered**: - **CATS**: - **HIF2A**: - **HSP90**: - **LeakyPDB**: - **MCL1**: - **Mpro**: - **SYK**: ## Uses BALM-Benchmark was initially created as a part of the BALM project (https://github.com/meyresearch/BALM) which fine-tunes Protein and Ligand Language Models by optimizing the distance between protein and ligand embeddings in a shared space using the cosine similarity metric that directly represents experimental binding affinity. Nevertheless, BALM-Benchmark can be used by itself, just like any other HuggingFace dataset: ```python from datasets import load_dataset # For instance, you want to load SYK data. Change the second argument into SYK syk_data = load_dataset("BALM/BALM-benchmark", "SYK", split="train") ``` As mentioned in the [Dataset Sources](#dataset-sources), the available datasets are: - `BindingDB_filtered` - `CATS` - `HIF2A` - `HSP90` - `LeakyPDB` - `MCL1` - `Mpro` - `SYK` Notice that all datasets only have one split (`train`). This is intentional such that the users can define their own splits, and can experiment with more random seeds for robustness. We highly recommend checking out different strategies for splitting the data (e.g., BindingDB) in [our BALM code repository](https://github.com/meyresearch/BALM/blob/refactor/balm/datasets/bindingdb_filtered.py#L157-L169). ## Citation **BibTeX:** ``` In preparation ``` ## Dataset Card Contact - Rohan Gorantla (rohan.gorantla@ed.ac.uk) - Aryo Pradipta Gema (aryo.gema@ed.ac.uk) - Antonia Mey (antonia.mey@ed.ac.uk)