OpenMMSLICER (Sequential LIgand Conformational ExploreR) is a Python library which uses OpenMM to perform Fully Adaptive Simulated Tempering (FAST) simulations to enhance the sampling of specific degrees of freedom using an alchemical approach. FAST combines adaptive alchemical sequential Monte Carlo (AASMC) with a variation of the irreversible simulated tempering algorithm (IST) that continuously optimizes the number, parameters, and weights of intermediate distributions.
OpenMMSLICER can be installed by creating the provided conda/mamba environment and then installing the package with pip:
git clone https://github.com/essex-lab/OpenMMSLICER.git
cd OpenMMSLICER
conda env create -f environment.yml
conda activate openmmslicer
python -m pip install .Full docstring documentation can be found here. There are also a few examples which you can run to see how OpenMMSLICER works.
If you have any problems or questions, please send an email to Justina Ratkeviciute at <jr1u18@soton.ac.uk>.
- M. Suruzhon, M. S. Bodnarchuk, A. Ciancetta, I. D. Wall, and J. W. Essex, “Enhancing ligand and protein sampling using sequential Monte Carlo,” J. Chem. Theory Comput. 18, 3894–3910 (2022), DOI:https://doi.org/10.1021/acs.jctc.1c01198
- M. Suruzhon, K. Abdel-Maksoud, M. S. Bodnarchuk, A. Ciancetta, I. D. Wall, and J. W. Essex, "Enhancing torsional sampling using fully adaptive simulated tempering," J. Chem. Phys. 160, 154110 (2024), DOI:https://doi.org/10.1063/5.0190659
- M. Suruzhon, J. Ratkeviciute, K. Abdel-Maksoud, A. Cavalleri, M. S. Bodnarchuk, A. Ciancetta, I. D. Wall, and J. W. Essex, "Fully Automatable Relative Binding Free Energy Calculations with Enhanced Sampling Using FAST/MBAR," J. Chem. Theory Comput. (2026), DOI:https://doi.org/10.1021/acs.jctc.6c00963