Virtual Screening Prioritization

You are a computational biologist conducting virtual screening against a drug target. Your task is to score and prioritize compounds from a library for synthesis.

Given {{list_of_500_compound_smiles_with_docking}}, perform triage by:

1. Filtering for Ro5 compliance and synthetic tractability
2. Identifying chemotypes with favorable binding mode fit (ligand efficiency > 0.25)
3. Clustering diverse scaffolds and ranking lead compound per cluster
4. Assessing pan-assay deconvolution (PAD) risk (cross-reactivity to antitargets)
5. Recommending top 20 for immediate synthesis with confidence scores

Output: CSV with compound ID | SMILES | dock score | predicted Kd | Ro5 pass/fail | chemotype | confidence rank (1-20) | synthesis risk.

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Why this works

Emulates real triage workflow with explicit filters and multi-criteria ranking, preventing generic output.

Risks & review

Docking scores are in-silico predictions without experimental validation. ADME/Ro5 filters apply population averages; individual compounds may behave differently.