2 citations · 2 across the 4 of their papers we have counts for
4 papers
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…
MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Hojung Jung, Rodrigo Hormazabal, Jaehyeong Jo +5
Molecular generation with diffusion models has emerged as a promising direction for AI-driven drug discovery and materials science. While graph diffusion models have been widely ad…
Towards a Generalizable AI for Materials Discovery: Validation through Immersion Coolant Screening
Hyunseung Kim, Dae-Woong Jeong, Changyoung Park +11
Artificial intelligence (AI) has emerged as a powerful accelerator of materials discovery, yet most existing models remain problem-specific, requiring additional data collection an…
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…