MolRGen is a benchmark and verifier for de novo molecular generation with reasoning LLMs using multi-objective rewards computed at generation time.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.
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MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
MolRGen is a benchmark and verifier for de novo molecular generation with reasoning LLMs using multi-objective rewards computed at generation time.
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Uncertainty-aware reinforcement learning for chemical language models
Uncertainty-aware RL for chemical language models raises true hit rate from 0.5 to 0.75 by favoring low-uncertainty regions during optimization.