ForgetU: Leaving Undesirable Molecules Behind in De Novo Design

Generative models based on flow matching and diffusion have shown strong potential for 3D de novo molecular design. However, models trained on existing molecular datasets may also learn to generate molecules with toxic or other undesired properties, limiting their reliability and practical utility. Retraining a model from scratch using only retained molecules can address this issue but may be computationally expensive for large 3D molecular datasets. Post-training a pretrained model provides an efficient alternative, but faces two key challenges: suppressing undesired generation while preserving the original generation capability, and preventing the forgotten behavior from being easily recovered through subsequent fine-tuning. In this study, we propose a Reinforcement Learning (RL)-based robust forgetting framework for pretrained 3D molecular flow matching models. RL-based forgetting suppresses undesired generation, while selective distillation preserves generation behavior on retained molecules. We further simulate relearning on undesired molecules and use the resulting loss to further update the model, improving the robustness of forgetting against subsequent fine-tuning. Experiments show that our framework achieves effective and robust forgetting while preserving the original generation capability.