How to predict the behavior of molecules from scant data
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
The MIT professors collaborated to identify the first AI-discovered antibiotic, a scientific breakthrough that is widely recognized as a milestone in AI-driven drug discovery.
Read MoreChina’s faster, more efficient model is forcing U.S. competitors to rethink how they innovate.
Read MoreMIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.
Read MoreA new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
Read MoreMIT engineers used a machine-learning model to design nanoparticles that can deliver RNA to cells more efficiently.
Read MoreChemXploreML makes advanced chemical predictions easier and faster — without requiring deep programming skills.
Read MoreA new method lets users ask, in plain language, for a new molecule with certain properties, and receive a detailed description of how to synthesize it.
Read MoreStarting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
Read MoreUsing this model, researchers may be able to identify antibody drugs that can target a variety of infectious diseases.
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