Update README.md

README.md

@@ -31,10 +31,14 @@ MolE learns task-independent molecular representations of chemicals via Graph Is
 
 ## Long description
 
-MolE integrates molecular graph-based representation learning with gradient-boosted decision trees for predicting antimicrobial potential. The approach involves: 
+**MolE** is a non-contrastive self-supervised Graph Neural Network (GNN) framework that leverages unlabeled chemical structures to learn task-independent molecular representations. By combining a pre-trained MolE representation with experimentally validated compound-bacteria activity data, the project builds an antimicrobial prediction model that re-discovers recently reported growth-inhibitory compounds
+that are structurally distinct from current antibiotics. Using the model as a compound prioritization strategy, three human-targeted drugs are identified and experimentally confirm as growth-inhibitors of Staphylococcus aureus, highlighting MolE’s potential to accelerate the discovery of new antibiotics.
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+<!-- MolE integrates molecular graph-based representation learning with gradient-boosted decision trees for predicting antimicrobial potential. The approach involves: 
 1. **Representation learning:** A graph neural network (GINet) trained on 100,000 randomly sampled compounds to derive
 molecular embeddings from SMILES strings.
-2. **Prediction:** These embeddings are used as input to an **XGBoost** model that predicts antimicrobial activity scores across 40 bacterial strains, based on data from *Maier et al., 2018*. The model was developed by **Roberto Olayo Alarcon et al.**. Further information is available in the [paper](https://www.nature.com/articles/s41467-025-58804-4).
+2. **Prediction:** These embeddings are used as input to an **XGBoost** model that predicts antimicrobial activity scores across 40 bacterial strains, based on data from *Maier et al., 2018*. The model was developed by **Roberto Olayo Alarcon et al.**. Further information is available in the [paper](https://www.nature.com/articles/s41467-025-58804-4). -->
 
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