CUE: A Chemical Uncertainty-Aware Embedding Framework for Multimodal Drug Selectivity Prediction

Abstract Accurate prediction of drug selectivity is critical for prioritizing candidate compounds with reduced off-target effects in AI-based drug discovery. Although selectivity can be inferred indirectly from drug–target affinity (DTA) prediction, cumulative errors from affinity predictions across multiple targets can reduce reliability, motivating the development of methods that directly predict compound-level selectivity. We propose a Chemical Uncertainty-aware Embedding (CUE) framework that integrates molecular fingerprints and 2D molecular structure image embeddings. The two modalities are combined via Loss Trajectory Analysis for Uncertainty (LTAU)-based weighted fusion, which adaptively reweights features according to their predictive reliability. Across eight benchmark data sets, CUE achieved RMSE values ranging from 0.163 to 1.691 and outperformed affinity-based and quantitative structure–activity relationship (QSAR) baseline models. Furthermore, a virtual screening case study for EGFR(T790M/C797S) inhibitors further demonstrated its utility for identifying selective hits. The source code is available at https://github.com/jjjabcd/CUE.