Hongqiao Wang

Bayesian Uncertainty-Guided Fidelity Fusion for Bioactivity Prediction

This work develops the Bayesian Class-Attentive Transformer Network, a unified Bayesian framework that integrates classification-to-regression knowledge fusion, uncertainty quantification, and active learning for data-efficient molecular property prediction and establishes a generalizable paradigm for uncertainty-aware molecular modeling.