Bayesian Uncertainty-Guided Fidelity Fusion for Bioactivity Prediction.

Accurate prediction of molecular bioactivity is a fundamental goal in rational drug design but remains challenging due to data scarcity and label imbalance. To address these limitations, we propose a unified Bayesian framework that integrates classification-to-regression knowledge fusion, uncertainty quantification, and active learning for data-efficient molecular property prediction. Specifically, we develop the Bayesian Class-Attentive Transformer Network (BCATNet). This model learns activity patterns from abundant classification data and incorporates the predicted probabilities as informative priors to guide the subsequent Bayesian regression task. Structurally, BCATNet employs a cross-token attention mechanism to model nonlinear interactions between class-derived semantics and molecular structural features. Comparative experiments against conventional machine learning models, graph neural networks, pretrained molecular models, and classification-guided baselines further demonstrated that explicit classification-to-regression knowledge fusion can provide a competitive and data-efficient alternative to generic molecular pretraining. Under reduced regression supervision, BCATNet maintained lower prediction errors and stronger robustness than competing models, supporting its utility in label-scarce settings. Beyond accuracy, the Bayesian formulation generated uncertainty estimates that were informative for reliability assessment: high-uncertainty predictions showed larger regression errors, and uncertainty-based risk stratification separated low-, medium-, and high-risk molecular predictions. Finally, BCATNet uncertainty served as an effective acquisition signal in active learning, with uncertainty-driven strategies achieving the best final performance in most benchmark tasks. Overall, BCATNet establishes a generalizable paradigm for uncertainty-aware molecular modeling by bridging classification and regression tasks within a Bayesian framework, offering a principled route toward reliable, interpretable, and resource-efficient drug discovery.