Create README.md

README.md

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+---
+tags:
+- protein-protein interaction
+- binding affinity
+- protein language models
+- drug discovery
+- bioinformatics
+- multi-chain proteins
+- ppb affinity
+license: cc-by-nc-sa-4.0
+pipeline_tag: regression
+task_categories:
+- text-classification
+---
+
+## Dataset Description
+
+This repository provides several enhanced versions of the **PPB-Affinity dataset**, ready for both sequence and structure-based modeling of multi-chain protein-protein interactions. The original PPB-Affinity dataset was introduced in the paper "[PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery](https://www.nature.com/articles/s41597-024-03997-4)".
+
+This version of the dataset was prepared for the study: "*Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction*."
+
+The primary enhancements in this repository include:
+* Various levels of data filtration and processing.
+* The addition of pre-extracted "Ligand Sequences" and "Receptor Sequences" columns, making the dataset ready for use with sequence-based models without requiring PDB file parsing. For complexes with multiple ligand or receptor chains, the sequences are comma-separated.
+
+## Dataset Configurations
+
+This dataset offers four distinct configurations:
+
+### 1. `raw`
+* **Description**: Minimally processed data from the original PPB-Affinity dataset. Only annotation inconsistencies have been resolved (see Section 2.1.1 of "*Beyond Simple Concatenation...*" for details).
+* **Size**: 12,048 entries.
+* **Splits**: Contains a single `train` split encompassing all entries.
+* **How to load**:
+    ```python
+    from datasets import load_dataset
+
+    raw_ds = load_dataset(
+        "proteinea/ppb_affinity",
+        name="raw",
+        trust_remote_code=True
+    )["train"]
+    ```
+
+### 2. `raw_rec`
+* **Description**: Similar to the `raw` version, but with an additional step to recover missing residues in the protein sequences (see Section 2.1.2 of "*Beyond Simple Concatenation...*" for details).
+* **Size**: 12,048 entries.
+* **Splits**: Contains a single `train` split.
+* **How to load**:
+    ```python
+    from datasets import load_dataset
+
+    raw_rec_ds = load_dataset(
+        "proteinea/ppb_affinity",
+        name="raw_rec",
+        trust_remote_code=True
+    )["train"]
+    ```
+
+### 3. `filtered`
+* **Description**: This version includes additional cleaning and filtration steps applied to the raw with missing residues recovered data (see Section 2.1.2 of "*Beyond Simple Concatenation...*" for details on filtration). It comes with pre-defined train, validation, and test splits (see Section 2.1.3 of "*Beyond Simple Concatenation...*" for splitting methodology).
+* **Size**:
+    * Train: 6,485 entries
+    * Validation: 965 entries
+    * Test: 757 entries
+* **Splits**: `train`, `validation`, `test`.
+* **How to load**:
+    ```python
+    from datasets import load_dataset
+
+    dataset_dict = load_dataset(
+        "proteinea/ppb_affinity",
+        name="filtered",
+        trust_remote_code=True
+    )
+    train_ds = dataset_dict["train"]
+    val_ds = dataset_dict["validation"]
+    test_ds = dataset_dict["test"]
+    ```
+
+### 4. `filtered_random`
+* **Description**: This version uses the same cleaned and filtered entries as the `filtered` configuration but provides random 80%-10%-10% splits for train, validation, and test, respectively. The shuffling is performed with a fixed seed (42) for reproducibility.
+* **Size**: Same total entries as `filtered`, split as:
+    * Train: 6,565 entries
+    * Validation: 820 entries
+    * Test: 822 entries
+* **Splits**: `train`, `validation`, `test`.
+* **How to load**:
+    ```python
+    from datasets import load_dataset
+
+    dataset_dict = load_dataset(
+        "proteinea/ppb_affinity",
+        name="filtered_random",
+        trust_remote_code=True
+    )
+    train_ds = dataset_dict["train"]
+    val_ds = dataset_dict["validation"]
+    test_ds = dataset_dict["test"]
+    ```
+
+## Data Fields
+
+All configurations share a common set of columns. These include columns from the original PPB-Affinity dataset (refer to the original paper for more details), plus two new sequence columns:
+
+* **`Ligand Sequences`**: `string` - Comma-separated amino acid sequences of the ligand chain(s).
+* **`Receptor Sequences`**: `string` - Comma-separated amino acid sequences of the receptor chain(s).
+
+**Note on Sequences**: When multiple ligand or receptor chains are present in a complex, their respective amino acid sequences are concatenated with a comma (`,`) as a separator in the "Ligand Sequences" and "Receptor Sequences" fields.
+
+---