Update README.md

README.md

@@ -14,8 +14,7 @@ datasets:
 ## Model Description
 
 
-This model predicts receptor classes from peptide sequences using the [ESM2](https://huggingface.co/docs/transformers/model_doc/esm) (Evolutionary Scale Modeling) protein language model with esm2_t6_8M_UR50D pre-trained weights. It is fine-tuned on receptor prediction using datasets from [PROPEDIA](http://bioinfo.dcc.ufmg.br/propedia2/) and [PepNN](https://www.nature.com/articles/s42003-022-03445-2), as well as novel peptides experimentally confirmed to bind to their target proteins, with binding conformations determined by ClusPro. The name `pep2rec_cppp` reflects its peptide-to-receptor prediction capability trained on ClusPro, PROPEDIA, and PepNN data.
-
+This model predicts receptor classes, identified by their PDB IDs, from peptide sequences using the [ESM2](https://huggingface.co/docs/transformers/model_doc/esm) (Evolutionary Scale Modeling) protein language model with esm2_t6_8M_UR50D pre-trained weights. The model is fine-tuned for receptor prediction using datasets from [PROPEDIA](http://bioinfo.dcc.ufmg.br/propedia2/) and [PepNN](https://www.nature.com/articles/s42003-022-03445-2), as well as novel peptides experimentally validated to bind to their target proteins, with binding conformations determined using ClusPro, a protein-protein docking tool. The name `pep2rec_cppp` reflects the model's ability to predict peptide-to-receptor relationships, leveraging training data from ClusPro, PROPEDIA, and PepNN.
 It's particularly useful for researchers and practitioners in bioinformatics, drug discovery, and related fields, aiming to understand or predict peptide-receptor interactions.
 
 ## How to Use
@@ -67,6 +66,22 @@ for label, prob in zip(sorted_class_labels[:10], sorted_class_probabilities[:10]
 
 ```
 
+Which gives this output:
+
+```
+Predicted Receptor Class: 1JXP
+Top 10 Class Probabilities:
+1JXP: 0.7793
+2OIN: 0.0058
+1A1R: 0.0026
+2QV1: 0.0025
+3KEE: 0.0022
+3KF2: 0.0016
+5LAS: 0.0016
+1QD6: 0.0014
+6ME1: 0.0013
+2XCF: 0.0013
+```
 
 ## Evaluation Results