Efficient Retrosynthesis Prediction with Integral Flow Matching and Latent Inversion

Fast and accurate retrosynthesis prediction is desired for downstream drug discovery and synthetic planning tasks. Currently, training and sampling state-of-the-art discrete diffusion or flow-based models for retrosynthesis requires significant computational resources. In this work, we propose FlashRetro, an Integral Flow Matching (IFM) framework with Latent Inversion for efficient retrosynthesis prediction. FlashRetro learns finite-interval latent transport rather than local instantaneous velocity, directly matching the transport quantity required for one-step latent inversion and avoiding test-time numerical integration. For inference, FlashRetro uses a principled 1-NFE Latent Inversion rule that maps a noise latent conditioned on the product latent to the reactant latent with a single learned integral-transport update. Experimental results on standard benchmarks (USPTO-50K) show that FlashRetro achieves superior top@1 accuracy, outperforming prior discrete flow-matching methods by 13.5% and diffusion-based baselines by 14.2%, while its average inference time is only 5.1 ms per reactant—38$\times$ faster than multi-step diffusion-based models and nearly 22$\times$ faster than recent discrete flow models. These results show that FlashRetro provides an effective path toward highly efficient 1-NFE retrosynthesis prediction with flow-based models.