FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
International conference on machine learning , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
CoMole combines motif-aware graph diffusion with RL policy optimization to deliver controllable molecular generation that outperforms baselines on nine targets across materials and drug benchmarks while keeping high validity.
citing papers explorer
-
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
-
How Creative Are Large Language Models in Generating Molecules?
Large language models exhibit distinct creative patterns in molecule generation, including higher constraint satisfaction when more constraints are added, and this is the first work to reframe molecule generation abilities as creativity.
-
Controllable Molecular Generative Foundation Models
CoMole combines motif-aware graph diffusion with RL policy optimization to deliver controllable molecular generation that outperforms baselines on nine targets across materials and drug benchmarks while keeping high validity.