Equivariant Force Field Calibration for Flow-based Protein Design

Flow models show promise for molecular design due to their fast, expressive sampling capabilities. However, their applicability to complex biological systems remains limited by a lack of physical grounding, which leads to unrealistic or unstable molecular structures. This work presents a framework that integrates force field guidance with consistency-based training to improve the physical fidelity of flow-based generative models. We calibrate pretrained flow models using differentiable physical energy functions to steer generation toward low-energy and sterically valid conformations. A consistency-based training strategy enables accurate, robust generation with very few sampling steps, improving inference efficiency. We evaluate it on multiple protein design tasks, including full-atom structure generation and peptide binder modeling. Experiments show that our approach consistently improves physical plausibility and geometric stability, while enabling favorable trade-offs between structural diversity and computational cost. By unifying physical calibration and efficient sampling, we advance the scalability and reliability of flow-based molecular generation.