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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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats: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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats: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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats: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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s44163-025-00494-4","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T11:21:14Z","timestamp":1759231274000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Blood brain barrier permeability prediction with artificial intelligence and machine learning: a meta-review and future directions"],"prefix":"10.1007","volume":"5","author":[{"given":"Nadine","family":"Grant","sequence":"first","affiliation":[]},{"given":"Diego","family":"Machado Reyes","sequence":"additional","affiliation":[]},{"given":"Zefan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Leo","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Chunyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Pingkun","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"issue":"3","key":"494_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s11481-006-9025-3","volume":"1","author":"Y Persidsky","year":"2006","unstructured":"Persidsky Y, Ramirez SH, Haorah J, Kanmogne GD. 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