{
  "abstract": "Background: The blood–brain barrier (BBB), which restricts the brain penetration of most small molecules and almost all biologics, continues to be a significant hurdle in the development of drugs for the central nervous system (CNS). During early-stage screening, a reliable computational prediction of BBB permeability, typically expressed as log BB, can help reduce the experimental load. Methods: We provide a well-validated machine learning system created solely using the B3DB experimental database, which includes 7807 chemicals with BBB+/BBB− annotations and 1058 compounds with in vivo log BB values. Using the Mordred library, a carefully selected set of 40 two-dimensional chemical descriptors was calculated from SMILES notation without the use of artificial data augmentation. Stratified five-fold cross-validation was used to comprehensively benchmark the nine methods used in this study. Results: On a held-out test set (n = 212), gradient boosting produced the greatest regression performance, with R2 = 0.6043, RMSE = 0.4740 log units, and MAE = 0.3326, which is in line with the upper range recorded for experimental BBB datasets. On an internal test set (n = 1562), the corresponding classifier obtained an AUC-ROC of 0.9476 and a balanced accuracy of 0.8568; on an independent external validation set (n = 175), it achieved an AUC-ROC of 0.9137. Topological polar surface area was found by SHAP analysis to be the primary factor influencing BBB permeability, with lipophilicity and ionization-related characteristics being the second and third most important factors, respectively. Nonlinear relationships in accordance with accepted pharmacokinetic principles were validated using partial dependence analysis. Conclusion: This study provides a reliable technique for predicting BBB permeability in CNS drug discovery.",
  "abstract_shown": true,
  "affiliations": [
    "School of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea",
    "Department of Geriatrics, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 9 Skłodowskiej Curie Str., 85-094 Bydgoszcz, Poland",
    "Department of Toxicology and Bromatology, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, 2 Jurasza Str., 85-089 Bydgoszcz, Poland",
    "Department of Organic and Physical Chemistry, Medical University of Warsaw, 1 Banacha Str., 02-097 Warsaw, Poland"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saurabh Tiwari",
      "orcid": "0000-0001-5029-9774"
    },
    {
      "affs": [
        1
      ],
      "name": "Katarzyna Mądra-Gackowska",
      "orcid": "0000-0003-1208-0290"
    },
    {
      "affs": [
        2
      ],
      "name": "Marcin Gackowski",
      "orcid": "0000-0001-9176-7108"
    },
    {
      "affs": [
        0
      ],
      "name": "Nokeun Park",
      "orcid": "0000-0002-7863-1457"
    },
    {
      "affs": [
        3
      ],
      "name": "Łukasz Szeleszczuk",
      "orcid": "0000-0002-5302-3797"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/br0xv7gaza7mf.bib",
  "cited_by": 5,
  "doi": "10.3390/pharmaceutics18060670",
  "fulltext_url": null,
  "id": "br0xv7gaza7mf",
  "issns": [
    "1999-4923"
  ],
  "landing_url": "https://doi.org/10.3390/pharmaceutics18060670",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3002|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7163386206",
  "pdf_url": null,
  "pmcid": "PMC13306079",
  "pmid": "42357286",
  "published": "2026-05-28",
  "publisher": "Multidisciplinary Digital Publishing Institute",
  "ris": "https://research.draconitic.com/paper/br0xv7gaza7mf.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:21:00Z",
      "native_id": "W7163386206",
      "source": "openalex",
      "url": "https://doi.org/10.3390/pharmaceutics18060670"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:3002|Computational Drug Discovery Methods",
      "subfield": "medicinal"
    }
  ],
  "title": "Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework",
  "title_text": "Predicting Blood–Brain Barrier Permeability from Experimental Data: An Interpretable and Externally Validated Machine Learning Framework",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:17Z",
  "url": "https://research.draconitic.com/paper/br0xv7gaza7mf",
  "venue": "Pharmaceutics",
  "versions": []
}