An AI-Integrated Approach to Nano-Based Drug Delivery Systems: Advances in Targeting and Smart Nano-Carriers
Bibliographic record
Abstract
Nanotechnology has changed drug delivery by enabling precise targeting and controlled release through nanocarriers like liposomes and polymeric nanoparticles. These systems increase drug stability, bioavailability, and therapeutic efficacy. However, optimizing drug delivery parameters stays a complex challenge due to the variability in nanoparticle properties and their interactions with biological environments. To address this, machine learning models were applied to predict drug release efficiency based on key physicochemical and pharmacokinetic parameters. Various models, including XGBoost, CatBoost, Random Forest, and Support Vector Regressor, were evaluated. Among these, XGBoost and CatBoost demonstrated superior predictive performance, identifying nanoparticle size, zeta potential, and drug-carrier ratio as critical determinants of drug release. This study highlights the role of artificial intelligence in accelerating drug formulation, reducing experimental workload, and optimizing nanoparticle-based delivery systems. Integrating AI-driven predictive modeling can significantly increase the efficiency and effectiveness of next-generation drug delivery strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".