README.md

metadata

license: cc-by-nc-nd-4.0
tags:
  - chemistry
  - bioinformatics
  - drug-discovery
  - blood-brain-barrier
  - graph-neural-network
configs:
  - config_name: classification
    data_files:
      - split: train
        path: classification/train-*
      - split: validation
        path: classification/validation-*
      - split: test
        path: classification/test-*
  - config_name: regression
    data_files:
      - split: train
        path: regression/train-*
      - split: validation
        path: regression/validation-*
      - split: test
        path: regression/test-*
dataset_info:
  - config_name: classification
    features:
      - name: SMILES
        dtype: string
      - name: label
        dtype: int64
      - name: InChI_Key
        dtype: string
      - name: scaffold
        dtype: string
    splits:
      - name: train
        num_bytes: 1040685
        num_examples: 7593
      - name: validation
        num_bytes: 106674
        num_examples: 727
      - name: test
        num_bytes: 136667
        num_examples: 942
    download_size: 515702
    dataset_size: 1284026
  - config_name: regression
    features:
      - name: SMILES
        dtype: string
      - name: label
        dtype: float64
      - name: InChI_Key
        dtype: string
    splits:
      - name: train
        num_bytes: 81036
        num_examples: 963
      - name: validation
        num_bytes: 7613
        num_examples: 84
      - name: test
        num_bytes: 9294
        num_examples: 100
    download_size: 56403
    dataset_size: 97943

BBBP Dataset

The paper is under review.

[Github Repo] | [Classification Model] | [Regression Model] | [Cite]

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.

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.

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.

License

This model and associated code are released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this model and its derivatives, which include models trained on outputs from the model or datasets created from the model, is prohibited and requires prior approval. Downloading the model requires prior registration on Hugging Face and agreeing to the terms of use. By downloading this model, you agree not to distribute, publish or reproduce a copy of the model. If another user within your organization wishes to use the model, they must register as an individual user and agree to comply with the terms of use. Users may not attempt to re-identify the deidentified data used to develop the underlying model. If you are a commercial entity, please contact the corresponding author.

Contact

For any additional questions or comments, contact Fahad Saeed (fsaeed@fiu.edu).