SpeCliff: Spectral Decoupling of Molecular Graph Representations for Activity Cliff Prediction

Graph neural networks often fail on molecular activity cliffs, where small structural edits cause large potency changes. We trace this failure to a spectral mismatch in the standard encoder-plus-mean-pooling design. Local substituent edits produce high-frequency graph signals, while linear Laplacian propagation attenuates higher frequencies and mean pooling dilutes localized changes. Motivated by this view, we propose SPECLIFF, a dual-branch architecture that combines a low-pass scaffold branch with an explicit high-pass branch based on Chebyshev-approximated spectral graph wavelets and bridge-edge decomposition. Across all 30 MoleculeACE tasks, SPECLIFF achieves the best average RMSE and wins on the largest number of datasets compared with prior baselines. Ablation studies, frequency-response analysis, and case studies show that the high-pass branch is critical and concentrates signal around substituent edits that drive activity cliffs.