Dayhoff Labs finds energy links to computation in chemical networks
Researchers demonstrate that energy consumption determines computational expressivity in chemical reaction networks through a new preprint.
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AI models for chemistry and biochemistry
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Researchers demonstrate that energy consumption determines computational expressivity in chemical reaction networks through a new preprint.
The acquisition brings crystal structure prediction expertise to build fast chemical reactivity simulators for industrial and biological challenges while hiring talent.
It predicts enzyme reactivity for reactions never catalogued, opening new ways to search biochemical space.
The model predicts enzyme reactivity for uncatalogued reactions, as published in PNAS.
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