Teaching language models to speak chemistry: From design to synthesis
8Artificial Intelligence is transforming how we approach chemical research and synthesis. By teaching language models to understand and generate the language of chemistry, we have developed complementary AI systems that bridge the gap between computational design and experimental reality. Our large language model system, ChemCrow, represents one of the first demonstrations of an AI system directly controlling robotic synthesis platforms, successfully executing the synthesis of compounds including organocatalysts and chromophores. Complementing this, our small language model system, Saturn, currently the most sample-efficient molecular design algorithm, enables precise molecular generation with built-in synthesizability constraints. Saturn's innovations include direct optimization against retrosynthetic predictions and integration of building block availability, ensuring that generated molecules are practically accessible. Our work demonstrates how different scales of language models can work together to transform chemical research, from initial molecular design through to physical synthesis, potentially revolutionizing drug discovery, catalysis, and materials development.
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