Machine learning for predicting antimicrobial efficacy of periodontal gel formulations in vitro biofilm models
Bibliographic record
Abstract
Periodontal disease caused by dysbiotic biofilms poses a major challenge and predicting the efficacy of topical antimicrobial gels is limited by biofilm resistance and resource-intensive in vitro testing. Therefore, it is of interest to develop machine learning (ML) models to predict antimicrobial efficacy of novel gel formulations against multi-species periodontal biofilms. Hence, a total of 120 formulations with varying polymers, agents, concentrations and enhancers were tested using the Calgary Biofilm Device and efficacy data were used to train Random Forest, SVM, Gradient Boosting and Neural Network models. Gradient Boosting achieved the best performance (accuracy 92.8%, AUC-ROC 0.96), with antimicrobial type, concentration and polymer viscosity as key predictors. ML, particularly Gradient Boosting, offers a reliable tool for predicting periodontal gel efficacy, enabling faster formulation optimization and reducing the need for extensive laboratory screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".