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README.md

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+---
+tags:
+- chemistry
+- bioinformatics
+- drug-discovery
+- blood-brain-barrier
+- graph-neural-network
+---
+
+# BBBP Dataset
+The paper is under review.
+
+\[[Github Repo](https://github.com/pcdslab/BBBP-Hybrid)\] | 
+\[[Classification Model](https://huggingface.co/SaeedLab/BBBP-Classification)\] | \[[Regression Model](https://huggingface.co/SaeedLab/BBBP-Regression)\] | \[[Cite](#bibtex)\]
+
+## 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.
+
+## Dataset Details
+
+This dataset is an aggregation of different literature sources (please see the paper to check the references).
+
+### Classification Task
+
+The number of samples for BBB- and BBB+ is presented below (corresponding to TABLE I in the paper).
+
+| Set Name   |   BBB+ |   BBB- |
+| :--------- | -----: | -----: |
+| Training   |  4,506 |  2,902 |
+| Validation |    557 |    370 |
+| Test       |    573 |    354 |
+| **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.
+
+## Dataset Usage
+
+### Classification
+
+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)
+```
+
+### Regression
+
+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)
+```
+
+## Citation
+
+The paper is under review. As soon as it is accepted, we will update this section.
+
+
+## Contact
+
+If you have any questions or encounter any issues, please reach out at [gabbianc@fiu.edu](mailto:gabbianc@fiu.edu).