A Classification-Based Blood–Brain Barrier Model: A Comparative Approach
Bibliographic record
Abstract
Background and Objectives: Drug permeability across the blood–brain barrier (BBB) remains a significant challenge in drug discovery, prompting extensive efforts to develop in silico predictive models. Most existing models rely on molecular descriptors to characterize drug properties. Feature selection algorithms play a crucial role in identifying the most relevant descriptors, thereby enhancing prediction accuracy. Methods: In this study, we compare the effectiveness of sequential feature selection (SFS) and genetic algorithms (GAs) in optimizing descriptor selection for BBB permeability prediction. Five different classifiers were initially trained on a dataset using eight molecular descriptors. Each classifier was then retrained using the descriptors selected by SFS and GA separately. Results: The results indicate that the GA method outperformed SFS, leading to a higher prediction accuracy (96.23%) when combined with a support vector machine (SVM) classifier. Furthermore, the GA approach, utilizing a fitness function based on classifier performance, consistently improved prediction accuracy across all tested models, whereas SFS showed lower effectiveness. Additionally, this study highlights the critical role of polar surface area in determining drug permeability across the BBB. Conclusions: These findings suggest that genetic algorithms provide a more robust approach than sequential feature selection for identifying key molecular descriptors in BBB permeability prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".