Update README.md

README.md

@@ -55,8 +55,7 @@ molecular embeddings from SMILES strings.
 - **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.
+    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)