Update README.md

README.md

@@ -389,6 +389,10 @@ The process involved the following steps:
    - Error handling was implemented for cases where molecules couldn't be processed.
    - The labels were resampled to ensure a balanced range of examples ([0,1])
    - The similarity scores were rounded to two decimal places for training.
+9. **Splitting**:
+   - The dataset was split into 80% for training, 10% for validation, and 10% for testing.
+   - For the Natural Products set (NP), the test set is labeled "NP-iso-base" because the SMILES strings were not canonicalized (isomeric forms were retained).
+   - The test set from ChemBL34 was then combined with the NP test set, and this combined set is referred to as "combined."
 
 This methodology aims to provide a diverse set of molecule pairs with labels indicating structural similarity. The combination of complexity binning, balanced inter- and intra-strata sampling, and MACCS fingerprint similarity labeling is intended to capture a range of molecular complexities while providing chemically relevant labels for model training. 
 
@@ -530,7 +534,8 @@ then query similars based on the average embeddings (returned in 3.5s):
 (WIP)
 
 ## Validation by Docking-based Virtual Screening
-Validation by Docking-based Virtual Screening (DBVS) is finished with promising hit rates, but some of the methods used are adapted from my undergraduate thesis, which is still in progress and pending publication.
+Validation by Docking-based Virtual Screening (DBVS) is finished, showing promising hit rates — ranging from 26% to 58% within the top 100 hits using averaged embeddings of nAChR α4β2 partial agonists, depending on the threshold applied. 
+However, some of the methods used are adapted from my undergraduate thesis, which is still in progress and pending publication. 
 Detailed results and methodologies will be fully disclosed after the thesis is published.
 
 ## Testing Generated Embeddings' Clusters