use-cases/target-prioritization.md 2026-07-08 02:01 UTC to 2026-07-14 17:03 UTC

name: Prioritize drug targets tagline: Rank drug targets across multiple evidence lanes. summary: Use ChatGPT with the Life Science Research plugin to normalize entities, retrieve genetics, cohort, clinical, literature, and expression evidence in parallel, score each lane, and produce a final ranking with reusable visuals. skills:

• token: Life Science Research url: codex://plugins/life-science-research@openai-curated description: Search scientific databases and literature to ground pathway, translational, tractability, and competitive evidence. bestFor:

• Target prioritization questions that need more than one evidence family, such as genetics, cohort replication, disease context, clinical precedent, literature, and expression.

• Teams that want ChatGPT to perform scientific research across multiple evidence lanes, then reconcile the results into one conclusion.

• Scientists who want saved raw payloads, an explicit scoring rubric, and visuals they can reuse in the next review or decision memo. starterPrompt: title: Prioritize Asthma Drug Targets body: >- Use the Life Science Research plugin to compare TSLP, IL33, and IL1RL1 for asthma target prioritization.

  Run these independent lanes in parallel with subagents:

  • Human genetics and GWAS: gwas-catalog-skill, opentargets-skill, gnomad-graphql-skill

  • Cohort replication and PheWAS: finngen-phewas-skill, ukb-topmed-phewas-skill, biobankjapan-phewas-skill, tpmi-phewas-skill

  • Target-disease evidence and disease context: opentargets-skill, efo-ontology-skill

  • Clinical and regulatory precedent: clinicaltrials-skill, opentargets-skill, chembl-skill, pharmgkb-skill

  • Literature and public-dataset context: ncbi-entrez-skill, ncbi-pmc-skill, biorxiv-skill, ncbi-datasets-skill, biostudies-arrayexpress-skill

  • Expression and tissue/cell-type context: human-protein-atlas-skill, gtex-eqtl-skill, cellxgene-skill, bgee-skill

  For each lane:

  • score TSLP, IL33, IL1RL1 on a 1-5 scale

  • keep direct asthma evidence separate from adjacent allergic/atopic phenotypes

  • save raw payloads when helpful

  Then synthesize:

  • a lane-by-target score table

  • a final rank of TSLP, IL33, IL1RL1

  • a confidence assessment and the main caveats

  • two visuals: a prioritization heatmap and a GWAS summary figure with the lead asthma-linked variants for each target suggestedEffort: high relatedLinks:

• label: Request access to GPT-Rosalind url: https://openai.com/form/life-sciences-access/

Leverage skills

The Life Science Research plugin includes skills for each evidence lane:

• Human genetics and GWAS: gwas-catalog-skill, opentargets-skill, gnomad-graphql-skill
• Cohort replication and PheWAS: finngen-phewas-skill, ukb-topmed-phewas-skill, biobankjapan-phewas-skill, tpmi-phewas-skill
• Target-disease evidence and disease context: opentargets-skill, efo-ontology-skill
• Clinical and regulatory precedent: clinicaltrials-skill, opentargets-skill, chembl-skill, pharmgkb-skill
• Literature and public-dataset context: ncbi-entrez-skill, ncbi-pmc-skill, biorxiv-skill, ncbi-datasets-skill, biostudies-arrayexpress-skill
• Expression and tissue/cell-type context: human-protein-atlas-skill, gtex-eqtl-skill, cellxgene-skill, bgee-skill

Use these skills by mentioning them specifically, or let ChatGPT decide when to use them.

Step-by-step guide

1. Start with a concrete comparison question and the exact targets, disease, and evidence lanes you want ChatGPT to cover.
2. Invoke the Life Science Research plugin and tell ChatGPT to run the lanes in parallel with subagents so each evidence family stays bounded.
3. Ask ChatGPT to score each lane on a fixed 1-5 scale and to keep direct disease evidence separate from adjacent phenotypes.
4. Review the saved raw payloads, the lane-by-target score table, and the synthesized rank in the same task.