Update README.md

README.md

@@ -178,7 +178,7 @@ This dataset collection has been refined and standardized, making it readily acc
 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
-BindingDB provides experimental binding affinity data (Kd values) for protein-ligand interactions. We focused on $K_d$ values due to inconsistencies in other affinity types. After filtering for computational efficiency and data consistency, the dataset comprises around 25,000 interactions with ~1,070 unique targets and 9,200 ligands. We implemented four data splits (Random, Cold Target, Cold Drug, and Scaffold) to evaluate generalizability, with splits based on ligand or protein novelty and structural dissimilarity, guided by the Murcko scaffold approach.
+BindingDB provides experimental binding affinity data (Kd values) for protein-ligand interactions. We focused on K_d values due to inconsistencies in other affinity types. After filtering for computational efficiency and data consistency, the dataset comprises around 25,000 interactions with ~1,070 unique targets and 9,200 ligands. We implemented four data splits (Random, Cold Target, Cold Drug, and Scaffold) to evaluate generalizability on test set  with splits based on unseen proteins, ligands and ligand scaffolds, guided by the Murcko scaffold approach.
 
 ### LP-PDBBind
 Derived from PDBBind v2020, LP-PDBBind is a curated collection of ~20,000 protein-ligand structures with experimental binding data. This dataset was reorganized to reduce similarity across splits and cleaned to remove covalently bound ligands and rare atomic elements. To ensure model reliability, we used Clean Level 1 (CL1) for training and the higher-quality CL2 data for validation and testing as recomended [here](https://pubmed.ncbi.nlm.nih.gov/37645037/). Here we provide 1D data, for 3D complexes please download from [here](https://github.com/THGLab/LP-PDBBind/).
@@ -190,7 +190,7 @@ The USP7 dataset, developed by [Shen et al.](https://jcheminf.biomedcentral.com/
 Collected as part of the [COVID Moonshot project](https://www.science.org/doi/10.1126/science.abo7201), the MPro dataset focuses on inhibitors targeting the SARS-CoV-2 main protease. The final cleaned dataset includes 2,062 ligands with IC50 values, converted to pIC50 for stability in training.
 
 ### 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.
+Selected from the protein-ligand free energy benchmark by [Hahn et al.](https://livecomsjournal.org/index.php/livecoms/article/view/v4i1e1497) with 21 target systems, we selected three targets to evaluate the deep learning model: 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
 
@@ -200,7 +200,7 @@ Selected from the protein-ligand free energy benchmark by [Hahn et al.](https://
     - **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 $pK_d$.
+    - **Y** (`float32`): binding affinity value in pKd.
 - **Mpro**:
     - **Index** (`string`): Index of the ligand-target pair. 
     - **Y** (`float32`): binding affinity value in pIC50.
@@ -225,8 +225,8 @@ Selected from the protein-ligand free energy benchmark by [Hahn et al.](https://
     - **CL2** (`bool`): Boolean indicating whether the complex belongs to Clean Level 2 (CL2) in the LP-PDBBind dataset.
     - **CL3** (`bool`): Boolean indicating whether the complex belongs to Clean Level 3 (CL3) in the LP-PDBBind dataset.
     - **remove_for_balancing_val** (`bool`): Boolean indicating if the entry is excluded for balancing in validation sets.
-    - **kd/ki** (`string`): Original binding affinity measurement (Kd or Ki), typically with units.
-    - **Y** (`float32`): Binding affinity value provided in log scale ($pK_d$).
+    - **kd/ki** (`string`): Original binding affinity measurement (Kd or Ki) with units (uM or nM).
+    - **Y** (`float32`): Binding affinity value provided in log scale (pKd).
     - **covalent** (`bool`): Boolean indicating if the ligand is covalently bound to the protein.
 - **HIF2A, MCL1, and SYK**:
     - **Index** (`string`): Index of the ligand-target pair.