Breaking the Timescale Barrier: Generative Discovery of Conformational Free-Energy Landscapes and Transition Pathways

TL;DR

Gen-COMPAS is a novel generative framework that efficiently reconstructs molecular transition pathways and free-energy landscapes without predefined variables, significantly reducing computational cost and enhancing mechanistic understanding.

Contribution

This work introduces Gen-COMPAS, a generative, committor-guided path sampling method that reconstructs transition pathways without relying on arbitrary variables, improving efficiency and accuracy.

Findings

Successfully applied to diverse molecular systems from miniproteins to membrane proteins.

Achieved rapid convergence of transition-path ensembles within nanoseconds.

Reconstructed free-energy landscapes and transition states accurately.

Abstract

Molecular transitions -- such as protein folding, allostery, and membrane transport -- are central to biology yet remain notoriously difficult to simulate. Their intrinsic rarity pushes them beyond reach of standard molecular dynamics, while enhanced-sampling methods are costly and often depend on arbitrary variables that bias outcomes. We introduce Gen-COMPAS, a generative committor-guided path sampling framework that reconstructs transition pathways without predefined variables and at a fraction of the cost. Gen-COMPAS couples a generative diffusion model, which produces physically realistic intermediates, with committor-based filtering to pinpoint transition states. Short unbiased simulations from these intermediates rapidly yield full transition-path ensembles that converge within nanoseconds, where conventional methods require orders of magnitude more sampling. Applied to systems from a miniprotein to a ribose-binding protein to a mitochondrial carrier, Gen-COMPAS retrieves committors, transition states, and free-energy landscapes efficiently, uniting machine learning and molecular dynamics for broad mechanistic and practical insight.