Machine learning for predicting antimicrobial efficacy of periodontal gel formulations in vitro biofilm models

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 <i>in vitro</i> testing. Therefore, it is of interest to develop machine learning (ML) models to predict <i>antimicrobial efficacy</i> 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 <i>viscosity</i> 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.