Update README_mcpp-dataset.md

README_mcpp-dataset.md

@@ -1,23 +1,44 @@
-Unified all-atom molecule generation with neural fields - MCPP Dataset
+# Unified All-Atom Molecule Generation with Neural Fields — MCPP Dataset
 
-Starting from 641 protein–MCP complexes obtained from the RCSB PDB, we employed a “mutate-then-relax” approach to curate a dataset comprising 
-of 186,685 MCP–protein complexes. (mcpp_dataset.tar.gz). Our strategy consists of randomly mutating the MCPs at 1 to 8 different sites, 
-using a list of 213 distinct amino acids. We relaxing them using fast-relax in Rosetta, which involves iterative cycles of side-chain 
-packing and all-atom minimization. Then, we selected the best mutated complexes based on the lowest interface scores. The source dataset comprises 
-lengths ranging from 4 to 25 amino acids with an average of 10. 78% of the MCPs contain one or more non-canonical amino acids, i.e. any amino acid 
-that is neither L-canonical nor D-canonical. We split the dataset into train (train_data.pt), test (test_data.pt) and validation (val_data.pt) 
-subsets using a clustering approach creating a test set consisting of 100 protein pockets.
+We curated a dataset of **186,685 MCP–protein complexes** (`mcpp_dataset.tar.gz`) starting from **641 protein–MCP complexes** from the **[RCSB PDB](https://www.rcsb.org/)** using a **“mutate-then-relax”** strategy:
 
+## Dataset Generation Pipeline
 
+1. **Mutation:**  
+   MCPs were randomly mutated at **1 to 8 sites** using **213 distinct amino acids**.
 
-After downloading the dataset (mcpp_dataset.tar.gz), open the folder using:
+2. **Relaxation:**  
+   Mutated complexes were relaxed using **FastRelax in Rosetta**, which iteratively performs side-chain packing and all-atom minimization.
 
-tar -xvzf mcpp_dataset.tar.gz
+3. **Selection:**  
+   The best complexes were chosen based on **lowest interface scores**.
+
+---
+
+## Dataset Statistics
+- MCP lengths: **4–25 amino acids** (average 10)  
+- **78%** of MCPs contain one or more **non-canonical amino acids**  
+
+---
 
-The preprocessed split data from the mcpp_dataset can be found in :
-test set: test_data.pt
-train set: train_data.pt
-validation set: val_data.pt
+## Dataset Splits
+The dataset is split using a clustering-based approach. The **test set** covers **100 protein pockets**:
 
-To generate MCP samples with Funcbind, copy the [split]_data.pt files into mcpp_dataset.
+| Split         | File             |
+|---------------|----------------|
+| Training set  | `train_data.pt` |
+| Validation set| `val_data.pt`   |
+| Test set      | `test_data.pt`  |
 
+---
+
+## How to Use
+
+1. **Download and extract:**
+```bash
+tar -xvzf mcpp_dataset.tar.gz
+```
+2. **To generate MCP samples with Funcbind, :**
+```bash
+cp train_data.pt val_data.pt test_data.pt mcpp_dataset/
+```