MolWorld: Molecule World Models for Actionable Molecular Optimization
📰 ArXiv cs.AI
arXiv:2605.08954v1 Announce Type: cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores. A useful candidate should also be actionable: it should be reachable from known molecules through valid local structural transformations, so that it can be interpreted as a plausible revision within an evolving chemical series. Existing de novo and single-molecule opti
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Title: MolWorld: Molecule World Models for Actionable Molecular Optimization
Abstract:
arXiv:2605.08954v1 Announce Type: cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores. A useful candidate should also be actionable: it should be reachable from known molecules through valid local structural transformations, so that it can be interpreted as a plausible revision within an evolving chemical series. Existing de novo and single-molecule opti
Abstract:
arXiv:2605.08954v1 Announce Type: cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores. A useful candidate should also be actionable: it should be reachable from known molecules through valid local structural transformations, so that it can be interpreted as a plausible revision within an evolving chemical series. Existing de novo and single-molecule opti
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