RealFlux: A Counterfactual Explanation Framework for Molecular Graphs Inspired by Local Optimization in Medicinal Chemistry

To address out-of-distribution (OOD) bias and topological leakage in graph masking, together with the decoupling of localization and generation in the evaluated Counterfactual Masking (CM) pipeline, we propose RealFlux, a unified molecular graph counterfactual explanation framework inspired by local optimization in medicinal chemistry. RealFlux introduces native directional utility (Native-\(U\)) for forward-utility-based localization without an external gradient explainer and combines Context-Locked Enforcement (CLE) with In-Distribution Gating (IDG) for constrained local intervention and predictor-representation validation. Across three pharmacological classification tasks, RealFlux achieves 97.7%–98.3% chemical validity and significantly higher classification flip rate than the evaluated CM baselines in all six pairwise comparisons after Holm correction (BSR, 0.286–0.680). On the common-substructure masking benchmark, the combined prediction discrepancy reaches \(\lvert\Deltay\rvert=0.583\), with the same relative advantage across GIN, GraphSAGE, and GAT. Median per-molecule inference latency is 0.86 s, more than an order of magnitude lower than that of the evaluated 3D diffusion baseline. Overall, RealFlux provides a directionally controllable and computationally efficient framework for model-guided molecular counterfactual analysis.