Prediction Is Not Action: Reliability-Guided Analytic Refinement of Molecular Conformers

Molecular conformer generation is an important task in computational chemistry and drug discovery. Despite substantial progress in generative models, generated conformers can still exhibit residual local geometric deviations in bond lengths and bond angles. Correcting these deviations depends on the geometric state of the current input conformer: predicting plausible internal geometry does not by itself determine the corresponding coordinate update. We therefore introduce a compact prediction-to-action framework for post-generation conformer refinement that explicitly separates local geometry prediction from input-conditioned corrective action. The resulting local correction signals are mapped to Cartesian atomic displacements using analytic derivatives of local molecular geometry, while a separate magnitude-control mechanism regulates the extent of the final intervention. Experiments show that our method substantially improves strict local geometric validity while inducing only limited changes to the input conformers. Furthermore, a model trained only on conformers from a single upstream generator transfers zero-shot to outputs from an unseen generator, yielding substantial improvements in local geometry without retraining. Together with its compact parameterization, these results support prediction-to-action refinement as a lightweight and transferable post-generation approach for correcting residual local geometric errors.