Software Daily Summary
Software Daily Summary: What Happened on October 12, 2026
AI drug discovery’s data gap comes into focus
A review flags a shortage of usable data, while interviews examine AI’s effects on scientific training, professional work and pharma supply chains.
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Today in brief
A review in Nature Reviews Drug Discovery points to a shortage of usable data for AI drug discovery.1 Separate reflections and interviews consider how language models may change scientific training, how companies can deploy AI with imperfect data, and how automation could affect early-career work.234
Also worth knowing
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A researcher described language models being used for tasks such as protein structure design and questioned how scientific training should change as the tools take on more work.2
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At LogiPharma 2026, Mark Talens urged pharmaceutical companies to start using AI on real problems before fully cleaning their data, while adding safeguards and improving data quality during implementation.3
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Geoffrey T. Sanzenbacher argued that automating entry-level work without replacing its learning and feedback could weaken companies’ future leadership pipelines.4
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A review in Nature Reviews Drug Discovery identifies a shortage of usable data as a challenge for AI drug discovery.1