One signal

AI agents are changing how computational drug-discovery teams work, according to scientist Srijit Seal. He says tasks such as building pipelines, setting up software checks and preparing plots that once took weeks can now take a fraction of the time.

The shift is not simply faster coding. Seal says agents can bring unfamiliar methods into a workflow, while people still need to check data quality, compliance and whether the results make scientific sense. His practical challenge to researchers: compare human and agent work on the same task, record mistakes and how they were caught, and describe what happens in a real pipeline, including when things fail. A special issue on real-world AI drug-discovery case studies is accepting submissions until 15 December 2026, Drug Target Review reports.

Discuss: Should drug-discovery teams publish agent failures and workflow comparisons even when the results are messy, or wait for stronger evidence?

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