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A knowledge-driven framework for predicting single-cell responses for unprofiled drugs

Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01286-w Feng et al. introduce MAP, an artificial intelligence framework that integrates biological mechanism knowledge to predict how cells respond to chemical perturbation, improving generalization to untested drugs and prioritizing cancer drug candidates in virtual screening.

We haven't written up this one. Nature Machine Intelligence has the full story — the link below goes straight to it.

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What Actually Breaks When You Put AI Agents In Front Of Real Customers

I have spent the last year building AI automation systems for small businesses. Chatbots, multi agent workflows, the kind of stuff that looks great in a demo and then meets an actual customer who types "idk just fix it" and breaks everything. Most articles about AI agents talk about architecture.

  • Real users often provide contradictory, half-formed inputs that break AI agents.
  • Explicitly instructing the model to say "I don't know" when uncertain is crucial.
  • Maintenance is the most significant challenge due to frequent API and model changes.

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