Towards Conditional Generation of Minimal Action Potential Pathways for Molecular Dynamics
This work addresses the challenge of simulating efficient molecular transformations for researchers in computational biology, though it is incremental as it builds on existing generative methods.
The paper tackled the problem of generating low-energy transformation pathways between protein structures in molecular dynamics by incorporating potential energy into a conditional generative model, showing improved synthesis of realistic molecular trajectories.
In this paper, we utilized generative models, and reformulate it for problems in molecular dynamics (MD) simulation, by introducing an MD potential energy component to our generative model. By incorporating potential energy as calculated from TorchMD into a conditional generative framework, we attempt to construct a low-potential energy route of transformation between the helix~$\rightarrow$~coil structures of a protein. We show how to add an additional loss function to conditional generative models, motivated by potential energy of molecular configurations, and also present an optimization technique for such an augmented loss function. Our results show the benefit of this additional loss term on synthesizing realistic molecular trajectories.