A Comprehensive Machine Learning Framework for Predicting Blood-Brain Barrier Permeability Enhanced by Ant Colony Optimization

INTRODUCTION: The blood-brain barrier (BBB) severely restricts drug delivery to the central nervous system (CNS). Early, accurate in silico prediction of BBB permeability is a critical challenge in drug discovery. This study aimed to develop, validate, and optimize a robust machine learning framework for this task, with a specific focus on enhancing a Deep Neural Network (DNN) model using a bio-inspired Ant Colony Optimization (ACO) algorithm. METHODS: A dataset of 1957 compounds was curated and characterized by 212 molecular descriptors. The dataset was partitioned using a scaffold-based split. A suite of baseline models (Naive Bayes, k-NN, SVM, Random Forest, XGBoost, and a baseline DNN) was benchmarked. An ACO algorithm was then implemented to perform a systematic hyperparameter search for the DNN. The performance of all models was evaluated on an independent test set. RESULTS: The Random Forest model provided a strong baseline performance (AUC = 0.913). The ACO algorithm identified a superior DNN architecture. The final ACO-Optimized DNN achieved a state-of-the-art performance on the independent test set, with an AUC-ROC of 0.921, outperforming all baseline classifiers. An error analysis of misclassified compounds provided insights into the model's limitations. DISCUSSION: The successful performance gain highlights the advantage of metaheuristic optimization over traditional hyperparameter search approaches. The model demonstrated stability and strong generalization. CONCLUSION: The ACO-Optimized DNN provides a highly accurate tool for virtual screening in CNS drug discovery, and the methodology serves as a generalizable template for complex model optimization.