Update README.md

README.md

@@ -6,6 +6,23 @@ base_model:
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]
 (https://colab.research.google.com/github/MuthuS97/PIPES-M/blob/main/PIPES-M.ipynb)
 
+
+**PIPES-M**, a deep learning-based binary classifier designed to predict protease inhibitor (PI) activity from primary protein sequences.
+
+
+PIPES-M is a fine-tuned sequence classification model built on the **ESM-2** protein language model:  
+- Base model: `facebook/esm2_t30_150M_UR50D` (150 million parameters, 30 layers)  
+- Pre-trained on UniRef50 via masked language modeling  
+
+Fine-tuning was performed on a high-quality curated dataset comprising:  
+- Positive examples: known protease inhibitors (<250 AA) from the MEROPS database  
+- Negative examples: non-inhibitors selected from UniProt using sequence similarity and Pfam domain analysis  
+
+Training used sequence-only input, requiring no structural data. The classification head leverages evolutionary and physicochemical features encoded by ESM-2.  
+
+Maximum sequence length is fixed at 250 residues; longer sequences are truncated from the N-terminus, appropriate for the typical size range of small secreted inhibitors.
+
+
 ---
 license: creativeml-openrail-m
 ---