MolWorld expands a molecule-transfer graph using a world model to discover high-property molecules that maintain strong structural connectivity to known compounds for actionable optimization.
Molecular de-novo design through deep reinforcement learning.Journal of cheminformatics, 9(1):48
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SGRPO expands the utility-diversity Pareto frontier in biomolecular design by using supergroup sampling and leave-one-out diversity rewards combined with utility signals.
citing papers explorer
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MolWorld: Molecule World Models for Actionable Molecular Optimization
MolWorld expands a molecule-transfer graph using a world model to discover high-property molecules that maintain strong structural connectivity to known compounds for actionable optimization.
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Pushing Biomolecular Utility-Diversity Frontiers with Supergroup Relative Policy Optimization
SGRPO expands the utility-diversity Pareto frontier in biomolecular design by using supergroup sampling and leave-one-out diversity rewards combined with utility signals.