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Abstract future prediction in latent space significantly boosts LLMs' chemical reasoning and molecular outcome prediction capabilities.
Chemists can now precisely steer retrosynthesis to favor specific precursor sets, moving beyond simple reaction recovery to nuanced chemical design.
A single generative flow model can dynamically transform molecular graphs, handling variable size and connectivity changes for retrosynthesis without explicit edit ordering.
Visual representations of graphs can transform how models reason and learn, bridging a critical gap between symbolic and visual understanding in AI.
