Contrastive Learning of Coarse-Grained Force Fields
This provides a systematic tool for improving simulation accuracy in molecular biology, though it is incremental as it builds on existing contrastive learning and umbrella sampling techniques.
The authors tackled the problem of accurately parameterizing coarse-grained force fields for molecular simulations by introducing potential contrasting, a method that learns force fields to reproduce conformational distributions from all-atom simulations. They demonstrated its effectiveness on the Trp-cage protein, capturing folding thermodynamics using only α-Carbons, and showed transferability across protein datasets.
Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes. Methodologies for systematic parameterization of the underlying energy function, or force field that describes the interactions among different components of the system are of great interest for ensuring simulation accuracy. We present a new method, potential contrasting, to enable efficient learning of force fields that can accurately reproduce the conformational distribution produced with all-atom simulations. Potential contrasting generalizes the noise contrastive estimation method with umbrella sampling to better learn the complex energy landscape of molecular systems. When applied to the Trp-cage protein, we found that the technique produces force fields that thoroughly capture the thermodynamics of the folding process despite the use of only $α$-Carbons in the coarse-grained model. We further showed that potential contrasting could be applied over large datasets that combine the conformational ensembles of many proteins to ensure the transferability of coarse-grained force fields. We anticipate potential contrasting to be a powerful tool for building general-purpose coarse-grained force fields.