Abstract
Abstract
Purpose
Predicting drug half-life is crucial for pharmacokinetics, impacting dosing strategies and drug development. Traditional regression models estimate exact half-life values but often struggle with pharmacokinetic variability, making direct interpretation and clinical decision-making challenging. This study proposes a classification-based approach that categorizes drugs into short and long half-life classes using a 12-hour threshold, providing a more practical and interpretable alternative.
Methods
Molecular structures were processed using a Convolutional Neural Network (CNN), specifically a fine-tuned AlexNet, to extract high-level features. These extracted features served as inputs for a neural network classifier. A holdout validation strategy was applied, with data split into 70% for training, 15% for validation, and 15% for testing. Model performance was assessed based on classification accuracy and F1-score. Results The proposed model achieved an F1-score of 90.9% for the optimal feature dimension of 10. The accuracy rates were 96.2% on validation data and 92.3% on test data, demonstrating strong generalization capabilities. The classification framework outperformed regression-based approaches by addressing the inherent variability in drug half-life data and providing a more clinically relevant decision-making tool.
Conclusion
This study introduces an efficient and interpretable method for drug half-life classification, facilitating improved drug formulation and clinical decision-making. The findings highlight the advantages of classification in early-stage drug development, providing a scalable and robust approach for pharmacokinetic applications.