Accurate prediction of molecular properties is essential for accelerating drug discovery, yet current deep learning methods generally lack reliable uncertainty estimates, particularly at the atomic level. A limitation of current approaches is the reliance on global molecular uncertainty, which frequently masks localized ambiguities by averaging the uncertainty across the entire molecule. To address this, we introduce AUINet, an atomic uncertainty-aware iterative network that quantifies and refines uncertainty at the atomic scale. Built on a D-MPNN architecture, AUINet uses Monte Carlo dropout to estimate atom-level uncertainty and iteratively refines atomic features through uncertainty-guided updates. Comprehensive evaluations show that AUINet outperforms state-of-the-art models on molecular property benchmarks and protein-protein interaction inhibitor tasks under low-data conditions, all without requiring extensive pretraining. Crucially, rejection sampling experiments reveal that atom uncertainty provides a more robust error signal than classic molecular uncertainty. More importantly, AUINet provides chemically interpretable insights by pinpointing specific functional groups and structural motifs that contribute most to prediction uncertainty, as validated in solubility prediction and activity-cliff analysis. Overall, AUINet's precise localization of atomic-level uncertainty establishes a new paradigm for trustworthy molecular property prediction.
Jiayu Qian, Yukun Luo, Qingping Zhou et al.· Journal of Chemical Informat...· 0 citations
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.
Shiyang Bian, Yukun Luo, Hongqiao Wang et al.· Journal of Chemical Informat...· 0 citations