How to Use OpenFE Relative Binding Free Energy

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Ligand/Drug Design & Screening Molecular Dynamics & Simulation Free Energy / Binding Energy Molecular Dynamics Small Molecules

Use OpenFE Relative Binding Free Energy online to rank a positioned ligand series with alchemical simulations.

Relative binding free energy (RBFE) compares related ligands through explicit alchemical simulations in both protein-bound and solvent environments. The difference between those thermodynamic legs gives a pairwise binding free-energy change, making RBFE useful for lead-optimization campaigns where the question is how substitutions change affinity within a series.

The Neurosnap workflow accepts one protein structure and two or more SDF ligands whose 3D coordinates are already positioned in the same binding site. OpenFE plans the transformation network, assigns ligand parameters and charges, builds explicit-solvent systems, and runs the selected sampling protocol on GPU.

How OpenFE Relative Binding Free Energy Works

Upload a protein-only Input Structure and an Input Small Molecules series in SDF format. Each SDF must contain valid bond orders and a protein-relative 3D pose; SMILES and CCD entries are rejected because they do not carry the required bound coordinates.

Protocol Repeats, Lambda Windows, Equilibration Length, and Production Length control sampling rigor and runtime. The output includes pairwise DDG values and a network-level maximum likelihood estimate on a common relative scale. Interpret estimates together with their uncertainties and inspect incomplete edges before using the ranking for a decision.

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 Relative Binding Free Energy on Neurosnap

Using OpenFE Relative Binding Free Energy on Neurosnap could drastically accelerate GPU-accelerated relative binding free energies with automated OpenFE network planning and MBAR analysis.

  • Campaign-scale comparison: A connected ligand network turns pairwise transformations into a common relative ranking.
  • Pose-preserving inputs: Uploaded SDF coordinates define the binding poses used by the simulation.
  • Explicit thermodynamic cycle: Complex and solvent legs replace empirical affinity scoring with physical sampling.
  • Visible uncertainty: Per-edge and network outputs expose whether additional sampling may be needed.

How to Use OpenFE Relative Binding Free Energy on Neurosnap

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

  1. Access Neurosnap: Start by logging in to the Neurosnap website.
  2. Select Tool: From the list of available tools, choose OpenFE Relative Binding Free Energy.
  3. Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
  4. Run Tool: Submit the OpenFE Relative Binding Free Energy job and Neurosnap will execute it in the cloud, automatically notifying you as soon as your results are ready.
  5. 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 Relative Binding 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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