How to use OpenFE Hydration Free Energy Online

Use OpenFE Hydration Free Energy online for absolute small-molecule solvation calculations.

Hydration free energy measures the thermodynamic preference of a molecule for water relative to vacuum. These calculations are useful for force-field assessment, solvation studies, and understanding how molecular changes alter water affinity without introducing a protein environment.

The Neurosnap workflow accepts small molecules directly as SMILES, CCD codes, or SDF records. SMILES, CCD, and non-3D SDF inputs receive generated and minimized conformers because no protein-relative pose must be preserved. Existing coordinates from a valid 3D SDF are retained.

How OpenFE Hydration Free Energy Works

Add one or more Input Small Molecules, choose OpenFF Sage or GAFF2, and set the repeat and sampling lengths. OpenFE constructs solvated and vacuum thermodynamic states for each molecule and evaluates them independently.

The output reports one hydration free energy per molecule in kcal/mol. Review the uncertainty alongside each estimate; increasing repeats allows uncertainty estimation, while longer production sampling may improve convergence for flexible or chemically difficult molecules.

What is Neurosnap?

Neurosnap is the leading platform for bioinformatics and computational science focused on expanding access to powerful modeling and simulation tools. Because many state-of-the-art machine learning systems remain complex to install, configure, and scale, Neurosnap offers a clean, browser-based workspace that removes the burden of infrastructure management, dependency conflicts, and command-line tooling.

Built for biologists, chemists, and cross-disciplinary scientists, the platform enables advanced computational workflows without requiring expertise in software engineering or cloud architecture. Researchers can launch analyses through an intuitive interface, connect programmatically through a comprehensive API, and rely on automated resource management to scale workloads efficiently. By taking care of the underlying compute and operational complexity, Neurosnap allows teams to devote their energy to scientific progress and faster iteration. Security and data protection remain foundational principles, with clear safeguards outlined in our Terms of Use and Privacy Policy to ensure your work stays protected.

Advancing Discovery with OpenFE Hydration Free Energy on Neurosnap

Using OpenFE Hydration Free Energy on Neurosnap could drastically accelerate automated GPU hydration free energies from SMILES, CCD, or SDF molecule inputs.

No protein preparation: The workflow operates directly on isolated small molecules.
Automatic conformers: Molecule-only inputs can be embedded and minimized safely before simulation.
Independent batch calculations: Multiple molecules are evaluated in one submission.
Rigorous analysis: Explicit-solvent sampling and MBAR provide estimates with repeat-derived uncertainty.

How to Use OpenFE Hydration Free Energy on Neurosnap

To harness the capabilities of OpenFE Hydration Free Energy, researchers can follow this streamlined workflow within Neurosnap:

Access Neurosnap: Start by logging in to the Neurosnap website.
Select Tool: From the list of available tools, choose OpenFE Hydration Free Energy.
Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
Run Tool: Submit the OpenFE Hydration Free Energy job and Neurosnap will execute it in the cloud, automatically notifying you as soon as your results are ready.
Review Output: Explore your results through rich visualizations, including figures, plots, and interactive views designed to help you analyze findings with clarity and confidence.

Citations

Please cite the original work when using OpenFE Hydration Free Energy in publications or research outputs.

Alibay, I. et al. The Open Free Energy library. Zenodo, 2025. https://doi.org/10.5281/zenodo.8344247.

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.

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.

Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/

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