Screen thousands of target-disease pairs for evidence

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What you get

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Who it’s for

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How Phylo runs it

“It’s very robust, with almost zero errors. It gives you genuinely useful results, and it embeds into a high-throughput agentic workflow easily.” — Kexin Huang, Co-founder & CEO, Phylo

“Even in analysis tasks, there’s literature research underneath. For single-cell annotation, you need to find marker genes. To compare a paper’s findings against your own dataset, you need literature. To brainstorm a hypothesis, to understand a mechanism of action for a particular drug and disease, all of this requires a large volume of literature. That’s why we spent so much time making sure our literature search was the highest quality.” — Kexin Huang, Co-founder & CEO, Phylo

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Building the screen

def screen(targets, diseases, template, search):
    for target in targets:
        for disease in diseases:
            query = template.format(target=target, disease=disease)
            body = search(query=query, human=True, page_size=25)
            yield {
                "target": target,
                "disease": disease,
                "n": len(body["results"]),
                "top": body["results"][:5],      # keep DOIs for provenance
            }

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What to check before you trust it

• Validate the template on 50 cells first. A template that produces noise at cell 50 produces noise at cell 50,000, expensively.
• Watch for entity-name collisions. Short gene symbols and common disease words pull in unrelated literature. Spot-check the highest-volume cells — anomalously high counts usually mean an ambiguous name.
• Distinguish a plan cap from a real result. If responses report finding more than they returned, your matrix is measuring your ceiling, not the literature.
• Alert when a whole run returns nothing. Silence looks identical to a broken job.

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