How to Use Nesso-1

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Ligand/Drug Design & Screening Molecular Docking & Interactions Affinity Prediction Proteins Small Molecules Virtual Screening

Screen small molecules against one protein sequence with Nesso-1.

Nesso-1 predicts protein–small-molecule affinity from one protein sequence and ligand chemistry. On Neurosnap, each molecule in the submitted panel is scored independently against the same protein. It is a screening workflow, not a multi-ligand complex or cofactor model. The upstream model is available under Apache-2.0.

How Nesso-1 Works

The results CSV is ranked by ascending Affinity log10(IC50 / µM), where lower values indicate stronger predicted affinity. Predicted pIC50 is calculated as 6 minus this affinity value, so higher pIC50 indicates stronger predicted affinity. Input Index preserves each candidate's original input position.

Binder Probability is a classifier score, not a calibrated guarantee of binding. Ensemble Difference is the absolute difference between two affinity heads on their native scale, not a calibrated uncertainty estimate. An exactly zero Interface Entropy before rounding triggers Placement Warning because ligand placement is unreliable. Check the warning text rather than inferring failure from a displayed entropy rounded to 0.000. All predictions require experimental validation.

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 Nesso-1 on Neurosnap

Using Nesso-1 on Neurosnap could drastically accelerate sequence-based protein–ligand affinity screening and candidate prioritization.

  • One target, many candidates: Rank independent ligands against a shared protein sequence.
  • Flexible chemistry inputs: Submit SMILES, CCD codes, or supplied SDF structures.
  • Readable screening evidence: Compare affinity and pIC50 alongside classifier and placement diagnostics without treating them as experimental measurements.

How to Use Nesso-1 on Neurosnap

To harness the capabilities of Nesso-1, 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 Nesso-1.
  3. Provide Inputs: Provide all the inputs specified within the submission panel and optionally configure the tool as desired.
  4. Run Tool: Submit the Nesso-1 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 Nesso-1 in publications or research outputs.

Shenoy, N., et al. “Nesso-1: Accelerating Open-Source Binding Affinity Predictions.” bioRxiv (2026). https://doi.org/10.64898/2026.08.01.742196.

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

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