Virtual Screening Prioritization Prompt
Prompt
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 [PASTE: list of 500+ compound SMILES with docking scores, predicted binding affinity, and ADME properties], 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.
Why it works
Emulates real triage workflow with explicit filters and multi-criteria ranking, preventing generic output.
Watch out for
Docking scores are in-silico predictions without experimental validation. ADME/Ro5 filters apply population averages; individual compounds may behave differently.
Used by
Data Analysts