Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations

This repository contains the supporting data, models, and figure generation code for the publication titled "Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations".

Directory Structure

• data/: Contains the datasets used for training and testing the EMLE models, along with training logs.
• emle_models/: Contains the various EMLE models trained and used in this study.
• figures/: Includes Jupyter notebooks used to generate the figures presented in the publication, along with the figures themselves.

Dependencies

The following packages are required to run the notebooks in this repository:

• numpy
• pandas
• scipy
• matplotlib
• seaborn
• scikit-learn
• torch
• rdkit
• emle-engine

Associated Packages

• fes-ml: Enables hybrid ML/MM free energy calculations, with support for various alchemical modifications.
• emle-bespoke: A package which streamlines the training of EMLE models by automatic conformer sampling, QM energy evaluations, and parameter fitting, with modular components for flexible use.

Citation

If you use the code, data, or models from this repository in your research, please cite the following publication:

@article{Morado2025,
  title   = {Enhancing Electrostatic Embedding for ML/MM Free Energy Calculations},
  author  = {Author 1 and Author 2 and ...},
  journal = {Journal Name},
  year    = {Year},
  volume  = {Volume},
  pages   = {Pages},
  doi     = {DOI}
}