OpenFE Relative Binding Free Energy
Rank a positioned ligand series with OpenFE relative binding free energy calculations.
Overview
Compute relative binding free energies across a positioned ligand series with the OpenFE framework, pairwise alchemical transformations, and network-level maximum likelihood estimates.
Run OpenFE Relative Binding Free Energy on Neurosnap
The OpenFE Relative Binding Free Energy online webserver allows anybody with a Neurosnap account to run and access OpenFE Relative Binding Free Energy, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Runs alchemical free energy perturbation with the OpenFE framework on CUDA GPUs.
- Ranks a ligand series with pairwise DDG edges and network-level MLE estimates.
- Ligands are parameterized with OpenFF Sage or GAFF2 and AM1-BCC charges; proteins use AMBER ff14SB.
- Fully configurable lambda windows, sampling lengths, and repeats with server-side safety limits.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access OpenFE Relative Binding Free Energy using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using OpenFE Relative Binding Free Energy in publications or research outputs.
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Alibay, I. et al. The Open Free Energy library. Zenodo, 2025. https://doi.org/10.5281/zenodo.8344247. |
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Eastman, P. et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Computational Biology, 2017. https://doi.org/10.1371/journal.pcbi.1005659. |
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Shirts, M.R. & Chodera, J.D. Statistically optimal analysis of samples from multiple equilibrium states. The Journal of Chemical Physics, 2008. https://doi.org/10.1063/1.2978177. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |