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README.md

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 # **GPepT: A Language Model for Peptides and Peptidomimetics**
+![alt text](TOC.png)
 
 GPepT is a cutting-edge language model designed to understand and generate sequences in the specialized domain of peptides and peptidomimetics. It serves as a powerful tool for _de novo_ protein design and engineering. As demonstrated in our research, the incorporation of peptidomimetics significantly broadens the chemical space accessible through generated sequences, enabling innovative approaches to peptide-based therapeutics.
 
 ## **Model Overview**
 GPepT builds upon the GPT-2 Transformer architecture, comprising 36 layers and a model dimensionality of 1280, with a total of 738 million parameters. This decoder-only model has been pre-trained on a curated dataset of peptides and peptidomimetics mined from bioactivity-labeled chemical formulas in ChEMBL.
 
-To leverage GPepT’s pre-trained weights, input molecules must be converted into a standardized sequence-like representation of peptidomimetics using **Monomerizer** (available on GitHub). Detailed insights into the training process and datasets are provided in our accompanying publication.
+To leverage GPepT’s pre-trained weights, input molecules must be converted into a standardized sequence-like representation of peptidomimetics using [**Monomerizer**](https://github.com/tsudalab/Monomerizer/tree/main). Detailed insights into the training process and datasets are provided in our accompanying publication.
 
 Unlike traditional protein design models, GPepT is trained in a self-supervised manner, using raw sequence data without explicit annotation. This design enables the model to generalize across diverse sequence spaces, producing functional antimicrobial peptidomimetics upon fine-tuning.