Bayesian learning for accurate and robust biomolecular force fields

TL;DR

This paper introduces a Bayesian framework for learning molecular force field parameters directly from ab initio data, improving model accuracy, interpretability, and transferability for biological molecules.

Contribution

It presents a novel probabilistic approach that integrates data and model parameters, providing uncertainty quantification and enhancing the development of predictive biomolecular models.

Findings

Successfully applied to 18 molecular fragments relevant to biomolecules

Demonstrated improved transferability and interpretability of force fields

Validated on calcium binding to troponin, a key cardiac process

Abstract

Molecular dynamics is a valuable tool to probe biological processes at the atomistic level - a resolution often elusive to experiments. However, the credibility of molecular models is limited by the accuracy of the underlying force field, which is often parametrized relying on ad hoc assumptions. To address this gap, we present a Bayesian framework for learning physically grounded parameters directly from ab initio molecular dynamics data. By representing both model parameters and data probabilistically, the framework yields interpretable, statistically rigorous models in which uncertainty and transferability emerge naturally from the learning process. This approach provides a transparent, data-driven foundation for developing predictive molecular models and enhances confidence in computational descriptions of biophysical systems. We demonstrate the method using 18 biologically relevant molecular fragments that capture key motifs in proteins, nucleic acids, and lipids, and, as a proof of concept, apply it to calcium binding to troponin - a central event in cardiac regulation.