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[2602.17602] MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models
Summary
MolHIT introduces a novel framework for molecular graph generation using Hierarchical Discrete Diffusion Models, achieving state-of-the-art performance in AI-driven drug discovery.
Why It Matters
This research addresses critical limitations in existing molecular generation models, enhancing chemical validity and performance in drug discovery and materials science. The advancements presented in MolHIT could significantly impact the efficiency and effectiveness of molecular design processes.
Key Takeaways
- MolHIT improves molecular graph generation with a new diffusion model.
- Achieves near-perfect chemical validity, surpassing previous models.
- Demonstrates strong performance in multi-property guided generation.
Computer Science > Artificial Intelligence arXiv:2602.17602 (cs) [Submitted on 19 Feb 2026] Title:MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models Authors:Hojung Jung, Rodrigo Hormazabal, Jaehyeong Jo, Youngrok Park, Kyunggeun Roh, Se-Young Yun, Sehui Han, Dae-Woong Jeong View a PDF of the paper titled MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models, by Hojung Jung and 7 other authors View PDF HTML (experimental) Abstract: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 adopted due to the discrete nature of 2D molecular graphs, existing models suffer from low chemical validity and struggle to meet the desired properties compared to 1D modeling. In this work, we introduce MolHIT, a powerful molecular graph generation framework that overcomes long-standing performance limitations in existing methods. MolHIT is based on the Hierarchical Discrete Diffusion Model, which generalizes discrete diffusion to additional categories that encode chemical priors, and decoupled atom encoding that splits the atom types according to their chemical roles. Overall, MolHIT achieves new state-of-the-art performance on the MOSES dataset with near-perfect validity for the first time in graph diffusion, surpassing strong 1D baselines across multiple metrics. We further demonstrate strong per...
