Update README.md

README.md

@@ -22,9 +22,7 @@ language:
 
 ## Short description
 
-MolE learns task-independent molecular representations of chemicals vis Graph Isomorphism Networks (GINs). Combined with an XGBoost classifier it estimates the probability of a compound
-inhibiting bacterial growth. The model was developed by Roberto Olayo Alarcon et al. and more information can be found in the GitHub repository and the accompanying paper.
-
+MolE learns task-independent molecular representations of chemicals vis Graph Isomorphism Networks (GINs). Combined with an XGBoost classifier it estimates the probability of a compound inhibiting bacterial growth. The model was developed by Roberto Olayo Alarcon et al. and more information can be found in the [GitHub repository](https://github.com/rolayoalarcon/MolE) and the [accompanying paper](https://www.nature.com/articles/s41467-025-58804-4)GitHub repository and the accompanying paper.
 
 ## Model versions
 
@@ -34,21 +32,12 @@ inhibiting bacterial growth. The model was developed by Roberto Olayo Alarcon et
     Repository: <https://github.com/rolayoalarcon/mole_antimicrobial_potential>\
     Hugging Face Hub: `virtual-human-chc/MolE`
 
-
 ## Long description
 
-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.**.
+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).
 
-
 ## Metadata
 
 ### Input
@@ -78,7 +67,6 @@ Further information is available in the [paper](https://www.nature.com/articles/
 -   **Training data:** 100000 randomly sampled compounds from ChemBERTa for the pretraining of MolE and data from *Maier et al., 2018* containing the influence of 1197 marketed drugs on the growth of 40 bacterial strains for the XGBoost classifier
 -   **Publication:** [Nature Communications (2025)](https://www.nature.com/articles/s41467-025-58804-4)
 
-
 ### Output
 
 -   **Description:** For each compound, the model predicts growth inhibition scores for 40 different bacterial strains.
@@ -93,7 +81,6 @@ Further information is available in the [paper](https://www.nature.com/articles/
 -   **Example output file:**
     `examples/output/example_molecules_prediction.tsv`
 
-
 ## Installation
 
 Install the conda environment with all dependencies:
@@ -147,7 +134,6 @@ pred = mole.predict_from_smiles("examples/input/examples_molecules.tsv")
 print(pred)
 ```
 
-
 ## References
 
 1.  Roberto Olayo Alarcon et al., *MolE: Graph-based molecular representation learning for antimicrobial discovery*, [Nature Communications (2025)](https://www.nature.com/articles/s41467-025-58804-4).
@@ -156,14 +142,9 @@ print(pred)
 4.  GitHub repository: <https://github.com/rolayoalarcon/mole_antimicrobial_potential>.
 5.  Model weights (Zenodo DOI): <https://doi.org/10.5281/zenodo.10803099>.
 
-
 ## Copyright
 
-Code derived from <https://github.com/rolayoalarcon/MolE> is licensed
-under the **MIT License**, © 2024 Roberto Olayo Alarcon.
-Model weights are licensed under **Creative Commons Attribution 4.0
-International (CC BY 4.0)**, © 2024 Roberto Olayo Alarcon.
-Additional code © 2025 Maksim Pavlov, licensed under MIT.
+Code derived from <https://github.com/rolayoalarcon/MolE> is licensed under the **MIT License**, © 2024 Roberto Olayo Alarcon. Model weights are licensed under **Creative Commons Attribution 4.0 International (CC BY 4.0)**, © 2024 Roberto Olayo Alarcon. Additional code © 2025 Maksim Pavlov, licensed under MIT.
 
 <!-- # MolE - Antimicrobial Prediction