Follow the MEP: Scalable Neural Representations for Minimum-Energy Path Discovery in Molecular Systems
This provides a scalable solution for researchers in computational biology and chemistry to rapidly analyze rare molecular transitions, though it is incremental as it builds on existing neural and optimization techniques.
The paper tackles the challenge of discovering minimum-energy paths (MEPs) for conformational transitions in molecular systems, which are traditionally slow to compute, and demonstrates that their neural optimization method identifies a MEP in minutes on a GPU, matching a millisecond-scale molecular dynamics simulation that would take weeks.
Characterizing conformational transitions in physical systems remains a fundamental challenge, as traditional sampling methods struggle with the high-dimensional nature of molecular systems and high-energy barriers between stable states. These rare events often represent the most biologically significant processes, yet may require months of continuous simulation to observe. One way to understand the function and mechanics of such systems is through the minimum energy path (MEP), which represents the most probable transition pathway between stable states in the high-friction, low-temperature limit. We present a method that reformulates MEP discovery as a fast and scalable neural optimization problem. By representing paths as implicit neural representations and training with differentiable molecular force fields, our method discovers transition pathways without expensive sampling. Our approach scales to large biomolecular systems through a simple loss function derived from the path's likelihood via the Onsager-Machlup action and a scalable new architecture, AdaPath. We demonstrate this approach on two proteins, including an explicitly hydrated BPTI system with more than 3,500 atoms. Our method identifies a MEP that captures the same conformational change observed in a millisecond-scale molecular dynamics (MD) simulation in just minutes on a standard GPU, rather than weeks on a specialized cluster.