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README.md
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The paper is under review.
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\[[Github Repo](https://github.com/pcdslab/BBBP-Hybrid)\] |
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\[[Classification Model](https://huggingface.co/SaeedLab/BBBP-Classification)\] | \[[Regression Model](https://huggingface.co/SaeedLab/BBBP-Regression)\] | \[[Cite](#
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## Abstract
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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.
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The paper is under review.
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\[[Github Repo](https://github.com/pcdslab/BBBP-Hybrid)\] |
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\[[Classification Model](https://huggingface.co/SaeedLab/BBBP-Classification)\] | \[[Regression Model](https://huggingface.co/SaeedLab/BBBP-Regression)\] | \[[Cite](#citation)\]
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## Abstract
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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.
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