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Purpose <p>The blood–brain barrier (BBB) is a major obstacle in neurological drug development, restricting most drugs from entering the brain. To address this, computational models leveraging Artificial Intelligence (AI) and machine learning (ML) have been explored for predicting BBB permeability. This meta-review explores various computational strategies leveraging AI and ML to improve BBB permeability prediction.</p> Methods <p>31 publications were included in this review following a search in PubMed Central and in the Journal of Cheminformatics. Models are categorized into three groups: (1) traditional ML models using physiochemical descriptors, (2) graph/image-based models leveraging molecular structure, and (3) encoder-based methods using SMILES representations.</p> Results <p>Traditional ML models achieve greater predictive accuracy due to their reliance on explicitly defined features, whereas deep learning methods, particularly graph neural networks (GNNs), show promise but require large-scale datasets and pretraining. Encoder-based methods underperform compared to traditional ML and GNNs, likely due to inadequate feature extraction.</p> Conclusion <p>Despite advancements, challenges such as dataset biases, model interpretability, and the need for experimental validation remain. Future research should explore multi-modal integration and generative AI to enhance BBB permeability prediction and aid drug discovery.</p>

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Blood brain barrier permeability prediction with artificial intelligence and machine learning: a meta-review and future directions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Nadine Grant,
  • Diego Machado Reyes,
  • Zefan Yang,
  • Leo Wan,
  • Chunyu Wang,
  • Pingkun Yan

摘要

Purpose

The blood–brain barrier (BBB) is a major obstacle in neurological drug development, restricting most drugs from entering the brain. To address this, computational models leveraging Artificial Intelligence (AI) and machine learning (ML) have been explored for predicting BBB permeability. This meta-review explores various computational strategies leveraging AI and ML to improve BBB permeability prediction.

Methods

31 publications were included in this review following a search in PubMed Central and in the Journal of Cheminformatics. Models are categorized into three groups: (1) traditional ML models using physiochemical descriptors, (2) graph/image-based models leveraging molecular structure, and (3) encoder-based methods using SMILES representations.

Results

Traditional ML models achieve greater predictive accuracy due to their reliance on explicitly defined features, whereas deep learning methods, particularly graph neural networks (GNNs), show promise but require large-scale datasets and pretraining. Encoder-based methods underperform compared to traditional ML and GNNs, likely due to inadequate feature extraction.

Conclusion

Despite advancements, challenges such as dataset biases, model interpretability, and the need for experimental validation remain. Future research should explore multi-modal integration and generative AI to enhance BBB permeability prediction and aid drug discovery.