update readme

README.md

@@ -5,7 +5,6 @@ tags:
 - bioinformatics
 - drug-discovery
 - blood-brain-barrier
-- graph-neural-network
 configs:
 - config_name: classification
   data_files:
@@ -68,14 +67,14 @@ dataset_info:
   dataset_size: 97943
 ---
 
-# BBBP Dataset
+# BBB Dataset
 The paper is under review.
 
 \[[Github Repo](https://github.com/pcdslab/BBBP-Hybrid)\] | 
-\[[Classification Model](https://huggingface.co/SaeedLab/BBBP-Classification)\] | \[[Regression Model](https://huggingface.co/SaeedLab/BBBP-Regression)\] | \[[Cite](#citation)\]
+\[[Inference Model](https://huggingface.co/SaeedLab/TITAN-BBB)\] | \[[Cite](#citation)\]
 
 ## Abstract
-The blood-brain barrier is a critical interface of the central nervous system, preventing most compounds from entering the brain. Predicting BBB permeability is essential for drug discovery targeting neurological diseases. Experimental in vitro and in vivo assays are costly and limited, motivating the use of computational approaches. While machine learning has shown promising results, combining handcrafted chemical descriptors with deep learning embeddings remains underexplored. In this work, we propose a model that integrates atom-level embeddings derived from SMILES representations with descriptors from cheminformatics libraries. We also introduce a curated dataset aggregated from multiple literature sources, which, to the best of our knowledge, is the largest available for this task. Results demonstrate that our approach outperforms state-of-the-art methods in classification and achieves competitive performance in regression, highlighting the benefits of combining deep representations with domain-specific features.
+The blood-brain barrier (BBB) restricts most compounds from entering the brain, making BBB permeability prediction crucial for drug discovery. Experimental assays are costly and limited, motivating computational approaches. While machine learning has shown promise, combining chemical descriptors with deep learning embeddings remains underexplored. Here, we introduce TITAN-BBB, a multi-modal architecture that combines tabular, image, and text-based features via attention mechanism. To evaluate, we aggregated multiple literature sources to create the largest BBB permeability dataset to date, enabling robust training for both classification and regression tasks. Our results demonstrate that TITAN-BBB achieves 86.5% of balanced accuracy on classification tasks and 0.436 of mean absolute error for regression. Our approach also outperforms state-of-the-art models in both classification and regression performance, demonstrating the benefits of combining deep and domain-specific representations.
 
 ## Dataset Details
 
@@ -87,14 +86,14 @@ The number of samples for BBB- and BBB+ is presented below (corresponding to TAB
 
 | Set Name   |   BBB+ |   BBB- |
 | :--------- | -----: | -----: |
-| Training   |  4,506 |  2,902 |
-| Validation |    557 |    370 |
-| Test       |    573 |    354 |
+| Training   |  4,564 |  3,029 |
+| Validation |    434 |    293 |
+| Test       |    638 |    304 |
 | **Total** |  **5,636** |  **3,626** |
 
 ### Regression Task
 
-For the regression task, based on the classification dataset, only compounds with logBB values were utilized. This resulted in a subset with 850 samples for training, 103 samples for validation, and 103 samples for testing.
+For the regression task, based on the classification dataset, only compounds with logBB values were utilized. This resulted in a subset with 963 samples for training, 84 samples for validation, and 100 samples for testing.
 
 ## Dataset Usage
 
@@ -105,13 +104,7 @@ Use the code below to load the dataset for classification task.
 ```py
 from datasets import load_dataset 
 
-data_files = {
-    "train": "train_classification.csv",
-    "validation": "val_classification.csv",
-    "test": "test_classification.csv"
-}
-
-dataset_dict = load_dataset("SaeedLab/BBBP", data_files=data_files)
+dataset_dict = load_dataset("SaeedLab/BBB", "classification")
 ```
 
 ### Regression
@@ -121,13 +114,7 @@ Use the code below to load the dataset for regression task.
 ```py
 from datasets import load_dataset 
 
-data_files = {
-    "train": "train_regression.csv",
-    "validation": "val_regression.csv",
-    "test": "test_regression.csv"
-}
-
-dataset_dict = load_dataset("SaeedLab/BBBP", data_files=data_files)
+dataset_dict = load_dataset("SaeedLab/BBB", "regression")
 ```
 
 ## Citation