Defines Conditional Distribution Matching (CDM) as finding inputs whose induced conditional distributions match a target distribution and proposes the MLGD-F inference-time algorithm using pretrained diffusion models to solve it without retraining.
Automatic chemical design using a data-driven continuous representation of molecules.ACS central science, 4(2):268–276
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SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.
Generate-Select-Refine is an open-ended Bayesian optimization method that generates tasks and concentrates evaluations on the best one with only logarithmic regret overhead relative to standard single-task optimization.
AI4S-SDS uses sparse MCTS and differentiable physics alignment to generate valid solvent mixtures and identifies a competitive photoresist developer formulation.
A neural network pipeline ranks plausible multistep metabolic pathways after training binary classifiers on real versus artificially generated reactions from public databases and enzymatic templates.
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Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
SGRPO is a GRPO-style framework that constructs set-level diversity rewards via supergroup sampling and leave-one-out redistribution to expand the utility-diversity Pareto frontier in biomolecular design tasks.