ezscreen 0.1.3

GPU-accelerated virtual screening CLI — powered by Kaggle GPUs

ezscreen

GPU-accelerated virtual screening powered by Kaggle GPUs.

ezscreen is a CLI tool that runs molecular docking campaigns on Kaggle's free GPUs. It handles receptor preparation, ligand prep, ADMET filtering, Kaggle kernel submission, result download, and hit visualisation — all from a single interactive command.

Prerequisites

• Python 3.11+
• A Kaggle account with GPU quota and an API token (kaggle.json)
• (Optional) NVIDIA NIM API key for Stage 2 validation with DiffDock-L

Installation

pip install ezscreen

Optional: scrubber for enhanced ligand prep

forlilab/scrubber provides tautomer enumeration and pH-driven protonation. Not on PyPI — install separately:

pip install git+https://github.com/forlilab/scrubber.git

Without it, ezscreen falls back to RDKit-only preparation (still fully functional).

Setup

ezscreen auth

Prompts for your Kaggle kaggle.json path and optionally an NVIDIA NIM API key.

Quickstart

ezscreen run

Interactive wizard guides you through:

1. Receptor — PDB ID (auto-downloaded) or local .pdb file
2. Chain selection
3. Binding site — co-crystal ligand, residue list, P2Rank prediction, or blind
4. Ligand file — .smi, .smiles, or .sdf
5. ADMET filtering (Lipinski, Veber, PAINS, Brenk)
6. Search depth — Fast / Balanced / Thorough
7. Submit to Kaggle

When the run completes, results are downloaded automatically and displayed:

ezscreen view ezs-<run_id>

Commands

Command	Description
ezscreen run	Interactive screening wizard
ezscreen auth	Configure Kaggle and NIM credentials
ezscreen view <run_id>	Show results table + open 3D viewer
ezscreen download <run_id>	Re-download results for a completed run
ezscreen status	List all runs with status (--live to auto-refresh)
ezscreen resume <run_id>	Resume an interrupted run
ezscreen admet <file>	Standalone ADMET filtering on any SDF/SMI file
ezscreen validate <receptor> <hits>	Stage 2 re-docking via NVIDIA NIM DiffDock-L
ezscreen clean <run_id>	Delete Kaggle dataset and kernel artifacts

Features

• UniDock GPU docking — builds UniDock from source on Kaggle to match the installed CUDA toolkit, avoiding pre-built binary ABI mismatches
• Compound identity — results include the original name and SMILES from your input file alongside docking scores
• Artifact filtering — unphysical scores (< −15 kcal/mol) from GPU batching edge cases are automatically removed
• Resilient download — retries with exponential backoff; recovers scores locally from docked PDBQTs if the Kaggle download fails mid-transfer
• ADMET filtering — Lipinski, Veber, PAINS, Brenk rules applied locally before submitting to reduce wasted compute
• 3D viewer — self-contained py3Dmol HTML viewer for top poses
• DiffDock-L validation — native NVIDIA NIM integration for high-accuracy re-docking of top hits

License

Apache-2.0