Stacking Ensemble Neural Network for Chemical Safety Assessment: A Case Study of Thyroid Peroxidase and Natural Product Screening.

ABSTRACT: Stacking ensemble learning is a method to improve model generalization and robustness. Deep neural networks have demonstrated significant potential for predicting chemical properties due to their effectiveness in learning complex patterns within the chemical space. Nevertheless, an individual model may rely on a single molecular feature set that might not explicitly explain all of the relationships between drugs and targets. Integrating a stacking ensemble with deep learning (DL) and various molecular features could potentially enhance the learning process and improve the ability to capture complex relationships between molecular structures and bioactivities. Chemicals binding to thyroid peroxidase (TPO) are associated with thyroid dysfunction, highlighting the importance of assessing thei

SUBMITTER: Zetta DN 

PROVIDER: S-EPMC12290627 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature