Flow-Matching Based Refiner for Molecular Conformer Generation

Low-energy molecular conformer generation (MCG) is a foundational yet challenging problem in drug discovery. Denoising-based generative models, including diffusion and flow matching, have become the dominant paradigm for MCG and achieved state-of-the-art performance by transporting samples from a simple base distribution to the molecular conformer distribution. However, sequential sampling can accumulate errors, especially in low-SNR regimes where inference states deviate from their timestep-conditioned training distributions. This train-inference mismatch makes vector-field prediction on off-trajectory states unreliable, further propagating errors through denoising. To address this issue, we propose a mismatch-aware flow-matching framework for molecular conformer refinement. Trained on data-centered perturbations, the refiner corrects upstream conformers without requiring explicit estimates of their residual error. Experiments on GEOM-QM9 and GEOM-Drugs show consistent quality improvements across strong denoising-based generators, while preserving diversity and reducing the total number of sampling steps.