How to use PyPermM Membrane Permeability Online

Estimate passive membrane permeability from 3D small-molecule structures.

PyPermM implements the physics-based PerMM approach to estimate how a molecule partitions into and crosses lipid membranes. It predicts intrinsic log permeability for black lipid, plasma, blood-brain barrier, Caco-2, and PAMPA membranes, plus membrane binding energy. PyPermM models passive transport, so these estimates do not describe active efflux transporters. The result depends on the supplied 3D geometry: an uploaded 3D SDF keeps its coordinates, while SMILES and CCD inputs receive a generated conformer.

How PyPermM Membrane Permeability Works

PyPermM assigns atom types, ionization and dipole terms, and accessible surface areas from an explicit-hydrogen 3D structure. It evaluates molecular orientations at depths across a model membrane, producing an insertion-energy profile. The energy profile is converted to intrinsic permeability estimates using the PerMM calibrations. Use the output table to compare compounds within each membrane system and inspect the profile to see where insertion is energetically favorable or costly. Solution pH controls the optional ionization correction. These are model estimates and should be followed by assay measurements for decisions that depend on absolute permeability.

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 PyPermM Membrane Permeability on Neurosnap

Using PyPermM Membrane Permeability on Neurosnap could drastically accelerate passive membrane permeability screening and membrane insertion-energy analysis.

Five membrane systems: Compare intrinsic permeability estimates for BLM, plasma, BBB, Caco-2, and PAMPA membranes.
Energy profile: Inspect insertion energy across membrane depth rather than relying only on summary scores.
Flexible molecular input: Use 3D SDF structures or generate a reproducible conformer from SMILES and CCD entries.
Batch review: Export human-readable CSV tables for molecule comparison and downstream analysis.

How to Use PyPermM Membrane Permeability on Neurosnap

To harness the capabilities of PyPermM Membrane Permeability, 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 PyPermM Membrane Permeability.
Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
Run Tool: Submit the PyPermM Membrane Permeability 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 PyPermM Membrane Permeability in publications or research outputs.

Lomize AL, Hage JM, Schnitzer K, et al. PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules. Journal of Chemical Information and Modeling. 2019;59(7):3094-3099. https://doi.org/10.1021/acs.jcim.9b00225

Wagen C. PyPermM: permeability modeling made easy. https://github.com/rowansci/pypermm

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

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