Uncertainty-Guided Adaptive Local Adversarial Perturbation for Reliable Molecular Property Prediction

Abstract

Molecular property prediction is a fundamental task with wide-ranging applications in chemistry, materials science, and drug discovery. Beyond predictive accuracy, the reliability of model predictions, particularly their uncertainty awareness, is critical for high-stakes downstream applications. However, most existing methods predominantly optimize performance metrics while overlooking prediction reliability, often producing results that are both inaccurate and overconfident. To address this challenge, we propose an uncertainty-guided framework that integrates adaptive local adversarial perturbation with evidential deep learning for reliable molecular property prediction. The proposed method identifies sensitive molecular regions through gradient-based sensitivity analysis and applies locally adaptive perturbations whose magnitudes are dynamically modulated by predictive uncertainty. This approach explicitly challenges the model to preserve predictive stability under structure-aware perturbations, thereby enhancing its sensitivity to critical molecular substructures. Furthermore, we introduce an evidential calibration strategy that aligns uncertainty with the prediction discrepancy between clean and adversarial molecular views, enabling the learned evidence to faithfully reflect prediction stability. Extensive experiments on benchmark molecular property prediction datasets demonstrate that our approach achieves state-of-the-art predictive performance while providing more reliable uncertainty estimates than widely used uncertainty quantification methods. These results highlight the potential of the proposed framework as a reliable tool for risk-sensitive virtual screening and molecular decision-making. Our code is available at https://github.com/Ubehind/ugap.