PyScreener Docking Workflow

Overview

Prepare a docking workflow and call PyScreener sidecar when configured; otherwise emit runnable docking manifests. It is in the AI Spectra + Virtual Screening category and is intended to connect advanced spectra prediction, docking, and active-learning screening workflows.

When To Use It

• You need a focused workflow for pyscreener docking workflow without leaving ChemistryAtlas.
• You want a result that can be saved, shared, or chained into another chemistry app.
• You want the calculation assumptions and limitations visible next to the output.

Inputs

• text - Chemistry input - type: textarea - Use formulas, names, SMILES-like text, reactions, or key=value options. Heavier engines will plug into this same app surface.

Recommended Workflow

• Prepare molecules, spectra, proteins, or candidate libraries; run the sidecar-capable workflow; then inspect rankings, uncertainties, and failure diagnostics.
• Start with the smallest representative input, confirm the parser understood it, then scale to a larger list or workflow.
• Save the generated report when the result will feed a notebook entry, route review, model comparison, or team discussion.

Outputs

• A Markdown-style chemistry report with parsed inputs, assumptions, and calculated or predicted results.
• Structured tables when the app returns multiple compounds, reagents, routes, peaks, candidates, or model rows.
• Warnings, fallback notes, and sidecar availability messages when a specialized engine is not installed or not reachable.

Method And Backend Notes

This app has a runnable ChemistryAtlas backend path. Backend type: model. ChemistryAtlas roadmap MVP: runnable report now; specialist cheminformatics/model backend plugs into this app surface next. Docking workflows are sensitive to protein preparation, protonation, waters, cofactors, box placement, and scoring-function bias; inspect the prepared structures and controls.

How To Interpret Results

• Sidecar model quality depends on installation, model weights, preparation choices, and applicability domain; review raw files and controls before acting.
• Compare results across related molecules, controls, blanks, literature examples, or known reactions whenever possible.
• For decisions that affect safety, synthesis scale-up, biological testing, purchasing, or publication, verify with primary data and expert review.

Example Input

protein=target.pdb
ligands=aspirin,caffeine
center=0,0,0
box=20,20,20

Common Checks Before Acting

• Confirm names, salts, stereochemistry, tautomers, protonation state, and hydration state.
• Check units, concentrations, equivalent definitions, and significant figures.
• Record external database versions, model versions, sidecar availability, and any manual edits made after the app output.

Related Apps

• AI MS/MS Predictor (ai-msms-predictor)
• Spectrum Similarity Scorer (spectrum-similarity-scorer)
• Candidate Structure Ranker (candidate-structure-ranker)
• Formula + Spectrum Elucidation (formula-spectrum-elucidation)
• MolPAL Active-Learning Virtual Screening (molpal-active-learning-virtual-screening)

Acknowledgements And Validation

• ChemistryAtlas documentation and UI were prepared for chemistry discovery workflows.
• Where available, calculations may use open-source cheminformatics, reaction-informatics, spectra, docking, or machine-learning engines such as RDKit-family tooling, ASKCOS-style sidecars, ChemProp, ms-pred/ICEBERG, PyScreener, and MolPAL.
• Always verify important results against primary literature, official SDS records, instrument software, validated models, and local laboratory procedures.
• Model-driven outputs should include model version, training domain, uncertainty, and independent validation before operational use.