Update README.md

README.md

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 ## Model Details
 
+The total size of all models is around 55GB. 
+
+
+There are 4 models available:
+
+- `LM`: Ligand Module trained on the AD dataset.
+- `LM+Advs`: Ligand Module trained on the AD dataset with adversarial training.
+- `LMPM`: Protein Module trained on the AD dataset using the weights of the Ligand Module.
+- `PLA-Net`: Ligand Module + Protein Module + PLA-Net trained on the AD dataset.
+
+
+Each of them has 102 [targets](https://github.com/juliocesar-io/PLA-Net/blob/main/data/targets.md) models with 4 fold cross-validation. The folder structure is the following:
+
+```bash
+checkpoints/
+    LM/
+        BINARY_ada/
+            Fold1/
+                Best_Model.pth
+            Fold2/
+                Best_Model.pth
+            ...
+        ...
+    LM+Advs/
+        ...
+    LMPM/
+        ...
+    PLA-Net/
+        ...
+```
+
 ### Model Description
 
 **PLA-Net** is a deep learning model designed to predict interactions between small organic molecules (ligands) and any of the 102 target proteins in the Alzheimer's Disease (AD) dataset. By transforming molecular and protein sequences into graph representations, PLA-Net leverages Graph Convolutional Networks (GCNs) to analyze and predict target-ligand interaction probabilities. Developed by [BCV-Uniandes](https://github.com/BCV-Uniandes/PLA-Net).