Datasets:

BALM
/

BALM-benchmark

@@ -169,10 +169,12 @@ size_categories:
 This dataset collection has been refined and standardized, making it readily accessible for deep learning model training and testing on [Hugging Face](https://huggingface.co/datasets/BALM/BALM-benchmark), providing a structured foundation for advancements in target-based drug discovery.
 ## Dataset Details
 To benchmark our models, we utilized several publicaly available datasets, encompassing diverse protein-ligand interactions and binding affinity values. Key datasets include BindingDB (1D data with protein sequnces and SMILES), LP-PDBBind (containing 3D complexes), and other target-specific datasets such as USP7, MPro, and three targets from the protein-ligand free energy benchmark (SYK, HIF2A, and MCL1). These datasets capture a wide range of binding affinity measurements, allowing us to evaluate and compare model performance against traditional docking and free energy methods. All datasets have been meticulously cleaned and are available on Hugging Face as `BALM-Benchmark`.
 ### BindingDB
@@ -190,13 +192,6 @@ Collected as part of the [COVID Moonshot project](https://www.science.org/doi/10
 ### Protein-Ligand Free Energy Benchmark
 Selected from the protein-ligand free energy benchmark by [Hahn et al.](https://livecomsjournal.org/index.php/livecoms/article/view/v4i1e1497), this dataset includes three targets: MCL1, HIF2A, and SYK. These targets offer diverse interactions, allowing for robust comparison with alchemical free energy methods. The datasets contain 37, 25, and 43 ligands, respectively, for benchmarking model predictions against established free energy methods.
-- **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**:

 This dataset collection has been refined and standardized, making it readily accessible for deep learning model training and testing on [Hugging Face](https://huggingface.co/datasets/BALM/BALM-benchmark), providing a structured foundation for advancements in target-based drug discovery.
+- **Dataset Repository:** https://huggingface.co/datasets/BALM/BALM-benchmark
+- **Code Repository:** https://github.com/meyresearch/BALM
+- **Paper:** TBA
+- **License:** CC-BY-4.0
 ## Dataset Details
 To benchmark our models, we utilized several publicaly available datasets, encompassing diverse protein-ligand interactions and binding affinity values. Key datasets include BindingDB (1D data with protein sequnces and SMILES), LP-PDBBind (containing 3D complexes), and other target-specific datasets such as USP7, MPro, and three targets from the protein-ligand free energy benchmark (SYK, HIF2A, and MCL1). These datasets capture a wide range of binding affinity measurements, allowing us to evaluate and compare model performance against traditional docking and free energy methods. All datasets have been meticulously cleaned and are available on Hugging Face as `BALM-Benchmark`.
 ### BindingDB
 ### Protein-Ligand Free Energy Benchmark
 Selected from the protein-ligand free energy benchmark by [Hahn et al.](https://livecomsjournal.org/index.php/livecoms/article/view/v4i1e1497), this dataset includes three targets: MCL1, HIF2A, and SYK. These targets offer diverse interactions, allowing for robust comparison with alchemical free energy methods. The datasets contain 37, 25, and 43 ligands, respectively, for benchmarking model predictions against established free energy methods.
 ### Dataset Columns
 - **BindingDB_filtered**: