OpenBind-Consortium

OpenBind-Consortium / EV-A71_2A_benchmark

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Benchmarking tools and analysis scripts for processing and evaluating structural data from the OpenBind EV-A71 2A dataset

openbind.uk
19
0
100% credibility
Found May 08, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository shares a curated dataset of protein-ligand complexes for the EV-A71 2A protease with benchmarks, reference evaluations, and scripts to reproduce figures comparing docking, cofolding, and affinity prediction methods.

How It Works

1
🔬 Discover the protein dataset

You come across a collection of real-world snapshots of a virus protein grabbing onto drug-like molecules, perfect for testing prediction tools.

2
📥 Grab the data files

Download the ready-made folders of crystal pictures, binding strengths, and test results from the safe public storage links.

3
📂 Set up your analysis folder

Put all the files into one organized spot on your computer so everything is in place to explore.

4
📈 Make the comparison charts

Run the easy picture tool to create colorful graphs showing how different prediction methods perform on docking, folding, and binding guesses.

5
🔍 Check pocket matches and affinities

Review tables and visuals that reveal how well methods match shapes, similarities to known data, and predict grip strengths.

🎉 Master the benchmarks

Celebrate having clear insights into which tools shine for this virus protein, ready to improve your own drug discovery work.

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AI-Generated Review

What is EV-A71_2A_benchmark?

This Python repo delivers benchmarking tools for evaluating docking, cofolding, and affinity prediction models on the OpenBind EV-A71 2A dataset—a collection of 925 protein-ligand structures and 601 compounds with affinities for the EV-A71 2A protease. Run simple scripts to process predictions, compute RMSD/LDDT-PLI metrics, correlate affinities via RMSE/Spearman rho, and generate publication-ready plots/tables. It's built for reproducible analysis of structure-activity relationships in a single-target setup.

Why is it gaining traction?

Unlike scattered public datasets, it pairs high-quality crystallography with affinities across coherent series, enabling precise error analysis for classical tools like GNINA/Smina and AI methods like AlphaFold3. Free Apache 2.0 tools handle GPU-accelerated benchmarking on Linux/PC, outputting compound-level stats and success rates—ideal for quick method comparisons without custom pipelines. The Zenodo-hosted data and one-command figure reproduction lower barriers for benchmarking Python workflows.

Who should use this?

Computational chemists benchmarking docking/cofolding poses against ground-truth structures. Drug discovery teams testing affinity predictors on real SAR data. ML engineers fine-tuning structure-based models need a dense A71 dataset for validation.

Verdict

Grab it for niche EV-A71 benchmarking—solid docs and ready plots make it instantly useful despite 19 stars and 1.0% credibility score. Still early; lacks broad tests, but Apache/CC0 licensing invites contributions to mature it.

(187 words)

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