Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptides
This work addresses a bottleneck in Molecular Dynamics for fields like materials science and pharmacology by improving simulation efficiency and accuracy, though it appears incremental as it builds on existing data-driven approaches.
The paper tackled the challenge of balancing time cost and accuracy in Molecular Dynamics simulations by proposing Force-guided Bridge Matching, a conditional generative model that incorporates physics priors to learn full-atom time-coarsened dynamics targeting the Boltzmann-constrained distribution, achieving enhanced simulations verified on peptide datasets with comprehensive metrics and transferability to unseen systems.
Molecular Dynamics (MD) is crucial in various fields such as materials science, chemistry, and pharmacology to name a few. Conventional MD software struggles with the balance between time cost and prediction accuracy, which restricts its wider application. Recently, data-driven approaches based on deep generative models have been devised for time-coarsened dynamics, which aim at learning dynamics of diverse molecular systems over a long timestep, enjoying both universality and efficiency. Nevertheless, most current methods are designed solely to learn from the data distribution regardless of the underlying Boltzmann distribution, and the physics priors such as energies and forces are constantly overlooked. In this work, we propose a conditional generative model called Force-guided Bridge Matching (FBM), which learns full-atom time-coarsened dynamics and targets the Boltzmann-constrained distribution. With the guidance of our delicately-designed intermediate force field, FBM leverages favourable physics priors into the generation process, giving rise to enhanced simulations. Experiments on two datasets consisting of peptides verify our superiority in terms of comprehensive metrics and demonstrate transferability to unseen systems.