AI Research

    Guiding large language models to predict edit sequences for molecular synthesizability optimization

    Leveraging generative models to bridge the gap between theoretical drug design and real-world chemical synthesis

    Nature Machine Intelligence, Published online: 23 September 2026; drug design often generates molecules that cannot be synthesized. Li and Lai show how large language models can predict precise structural edits to fix these molecules, o...

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    Nature Machine Intelligence

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    Nature Machine Intelligence
    Guiding large language models to predict edit sequences for molecular synthesizability optimization
    AI Analysis

    Generated insight by OnAirToday AI

    This research bridges the gap between theoretical drug discovery and practical chemical manufacturing by teaching models to propose actionable molecular edits. By automating the optimization of synthesizability, this approach significantly accelerates the development timeline for viable therapeutic compounds.

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