Author

Hanbum Ko

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#artificial intelligence Preprint Sep 2026

MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design

Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy multiple constraints, identify infeasible specifications, and reason over multi-step tool outputs. Existing benchmarks do not capture this complexity, focusing instead on e...

Yongjun Jeong, Hanbum Ko, Yejun Kim et al. · 0 citations

#artificial intelligence Mar 2026

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction

Experimental results show that RetroReasoner outperforms prior baselines, including not only molecular LLMs but also retrosynthesis-specific expert models, and generates a broader range of feasible reactant proposals, especially for challenging reaction instances.

Hanbum Ko, Chanhui Lee, Yejun Kim et al. · arXiv.org · 1 citation