Source code to reproduce the work's preprint.
Given all publicly available RBFE benchmarking datasets, we have created a training domain ('RBFE-Space') that contains a representation of all perturbations present in these datasets by grafting them onto a common benzene scaffold. Then, after running all RBFE simulations for this novel set, we have used this training domain to train ML models to predict the quintuplicate standard error of the mean free energy (SEM). We have adjusted LOMAP to ingest these predicted SEM values to use instead of the native LOMAP-score, thereby producing a data-driven method of producing RBFE networks.
To reproduce, install the provided conda environment on a linux machine with at least one GPU (cuda). Main dependencies:
Main steps to reproduce:
-
Run
_01_SETUP_BENZENE_TRAINSET.ipynbto get the list of transformations in RBFE-Space -
Run
_02_SETUP_BSS_FOLDERS_TRAINSET.ipynbto set up RBFE input files using BioSimSpace. BSS can set up simulations for SOMD, Amber and Gromacs or export the files needed for other RBFE implementations. -
Run all RBFE simulations on a cluster
-
Collect SEM values from simulations in the format of
ANALYSIS/perturbation_networks/input/fepspace_sems_full_balanced.csv -
Sequentially run all python scripts/notebooks in
ANALYSIS/perturbation_networks/to reproduce the majority of RBFE network generation figures used in the paper. -
Other figures can be reproduced using the notebooks found in
ANALYSIS/fepspace_vs_free_vs_bound/andANALYSIS/lambda_spacing/
Please note that some files (e.g. simulation outputs) were not included in this repository due to github memory restrictions. Feel free to post an issue with any questions regarding this work.
Authors:
- J. Scheen
- M. Mackey
- J. Michel