3 papers
cs.AI2026
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…
cs.LG2025
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction
Jaewan Lee, Changyoung Park, Hongjun Yang +3
Recent advancements in graph neural networks (GNNs) have significantly enhanced the prediction of material properties by modeling crystal structures as graphs. However, GNNs often…
cs.LG2025
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…