Towards empirical force fields that match experimental observables

Abstract

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field.

Supporting information included in ancillary files. The article has been accepted by J. Chem. Phys

References in corpus (8)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A high-bias, low-variance introduction to Machine Learning for physicists
• Machine learning for molecular simulation
• Bayesian ensemble refinement by replica simulations and reweighting
• Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
• Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments
• Combining simulations and solution experiments as a paradigm for RNA force field refinement
• ATP dependent NS3 helicase interaction with RNA: insights from molecular simulations

Cited by in corpus (15)

• Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
• Universal QM/MM Approaches for General Nanoscale Applications
• Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
• Accurate machine learning force fields via experimental and simulation data fusion
• Integrating experimental data with molecular simulations to investigate RNA structural dynamics
• The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials
• chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
• Refinement of molecular dynamics ensembles using experimental data and flexible forward models
• Molecular simulations matching denaturation experiments for N6-Methyladenosine
• Boosting ensemble refinement with transferable force field corrections: synergistic optimization for molecular simulations
• Molecular dynamics simulations of chemically modified ribonucleotides
• MOLPIPx: an end-to-end differentiable package for permutationally invariant polynomials in Python and Rust
• Molecular simulations to investigate the impact of N6-methylation in RNA recognition: Improving accuracy and precision of binding free energy prediction
• MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data
• Automatic learning of hydrogen-bond fixes in an AMBER RNA force field