Update README.md

README.md

@@ -32,52 +32,43 @@ 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: 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).
+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
 
--   **Description:** Table of chemicals with their SMILES
-    representations.
--   **Shape:** `[n, 2]`
--   **Data format:**
-    -   `chem_name` *(str)*: Name of the molecule (e.g., *Halicin*).
-    -   `smiles` *(str)*: SMILES representation of the molecule (e.g.,
-        `C1=C(SC(=N1)SC2=NN=C(S2)N)[N+](=O)[O-]`).
--   **Example input file:** `examples/input/examples_molecules.tsv`
-
+- **Description:** Table of chemicals with their SMILES representations.
+- **Shape:** `[n, 2]`
+- **Data format:**
+  - `chem_name` *(str)*: Name of the molecule (e.g., *Halicin*).
+  - `smiles` *(str)*: SMILES representation of the molecule (e.g.,`C1=C(SC(=N1)SC2=NN=C(S2)N)[N+](=O)[O-]`).
+- **Example input file:** `examples/input/examples_molecules.tsv`
 
 ### Model
 
--   **Modality:** Probability of microbial growth inhibition.
--   **Scale:** Combination of bacterial strain and compound.
--   **Description:**
-    -   The model computes antimicrobial predictive probabilities for **40 bacterial strains** contained in [Maier et al., 2018](https://www.nature.com/articles/nature25979), using a two-step process:
-        1.  Generate molecular embeddings with a pre-trained **GINet**
-            representation model (`model.pth`, `config.yaml`).
-        2.  Predict antimicrobial properties with a **trained XGBoost**
-            classifier (`MolE-XGBoost-08.03.2024_14.20.pkl`).
-    -   The scores reflect the likelihood that a compound inhibits the
-        growth of a given bacterial strain.
--   **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)
+- **Modality:** Probability of microbial growth inhibition.
+- **Scale:** Combination of bacterial strain and compound.
+- **Description:**
+  - The model computes antimicrobial predictive probabilities for **40 bacterial strains** contained in [Maier et al., 2018](https://www.nature.com/articles/nature25979), using a two-step process:
+    1.  Generate molecular embeddings with a pre-trained **GINet** representation model (`model.pth`, `config.yaml`).
+    2.  Predict antimicrobial properties with a **trained XGBoost** classifier (`MolE-XGBoost-08.03.2024_14.20.pkl`).
+    - The scores reflect the likelihood that a compound inhibits the growth of a given bacterial strain.
+- **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.
--   **Output format:** table
-    -   **Shape:** `[n × 40, 2]`
-    -   **Columns**
-        -   `pred_id` *(str)*: Combination of the molecule name and
-            bacterial strain (e.g.,
-            `Halicin:Akkermansia muciniphila (NT5021)`).
-        -   `antimicrobial_predictive_probability` *(float)*: Predicted
-            probability that the compound inhibits microbial growth.
--   **Example output file:**
-    `examples/output/example_molecules_prediction.tsv`
+- **Description:** For each compound, the model predicts growth inhibition scores for 40 different bacterial strains.
+- **Output format:** table
+  - **Shape:** `[n × 40, 2]`
+  - **Columns:**
+    - `pred_id` *(str)*: Combination of the molecule name and bacterial strain (e.g., `Halicin:Akkermansia muciniphila (NT5021)`).
+    - `antimicrobial_predictive_probability` *(float)*: Predicted probability that the compound inhibits microbial growth.
+- **Example output file:** `examples/output/example_molecules_prediction.tsv`
 
 ## Installation