RetroVirtSyn: Adaptive Multi-Center Virtual Synthons for Single-Step Retrosynthesis

Two-stage single-step retrosynthesis is susceptible to error propagation because reactant generation depends critically on reaction-center predictions made in the preceding stage. Conventional semi-template methods typically retain a single reaction-center candidate and construct synthons through hard bond disconnection based on that prediction. When the predicted center is incorrect, synthon construction converts prediction errors into structural deviations, amplifying their impact on downstream reactant generation. To address this limitation, we propose RetroVirtSyn, a retrosynthesis framework based on adaptive multi-center virtual synthons. RetroVirtSyn avoids hard bond disconnection; instead, it adaptively retains multiple candidate reaction centers based on prediction confidence and encodes them as confidence-weighted probabilistic conditions to construct virtual synthons directly on the intact product graph. Empirically, RetroVirtSyn achieves 63.2% Top-1 accuracy on USPTO-50K, outperforming the previous state of the art by 3.2 percentage points, while also achieving the best Top-3, Top-5, and Top-10 accuracies among the compared methods. These results demonstrate that virtual synthons provide an effective interface between reaction-center prediction and reactant generation, mitigating error propagation and thereby improving retrosynthesis accuracy.