2 citations · 3 across the 3 of their papers we have counts for
4 papers
RetroPlanner: Multi-Route Expansion and Multi-Objective Reasoning for LLM-Based Retrosynthesis Planning
Ye Rin Kim, Yongjun Jeong, Seongjun Yun +4
Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be practical in real-world chemi…
MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design
Yongjun Jeong, Hanbum Ko, Ye Rin Kim +6
Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires them to interpret design contexts, satisfy multiple constraints, identify i…
RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction
Hanbum Ko, Chanhui Lee, Ye Rin Kim +4
Retrosynthesis prediction aims to identify reactants that can synthesize a given product molecule. Although molecular large language models (LLMs) have recently shown promising res…
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Chanhui Lee, Hanbum Ko, Yuheon Song +6
Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Larg…