Generative Models Predict Transition States for Unseen Chemical Reactions
bravo_abad · x · 2026-08-13
Generative models often suffer degraded performance when predicting transition-state geometries for chemical reactions involving out-of-domain elements or transition metals.
To address this fragility, Samir Darouich and coauthors introduce a data-efficient approach: pretraining on equilibrium conformers rather than actual reaction data. The researchers arrange molecular conformers into synthetic "pseudo-reactions"—using one conformer as a pseudo-reactant, the highest-energy conformer as a pseudo-transition state, and the lowest-energy as a pseudo-product. This pretraining exposes the model to realistic geometries and bonding environments before fine-tuning on scarce transition-state data, thereby significantly improving generalization.
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