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Insilico Medicine AI Limited
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Top-K prompting transforms retrosynthesis by capturing diverse reaction predictions, outperforming traditional models in accuracy and uniqueness.
LLMs may excel at strategic thinking, but they lag behind specialized models in practical synthesis planning, highlighting a critical gap in current AI capabilities.
The first clean nonconvex convergence guarantee for pipeline parallelism reveals surprising trade-offs between methods depending on the training objective.
Forget scaling up: a smaller, purpose-trained Liquid Foundation Model (LFM), guided by the MMAI Gym, beats larger general-purpose models at drug discovery tasks.
