6 citations · 13 across the 8 of their papers we have counts for
9 papers
Scaling RNA Foundation Models through Iterative Refinement
Pengan Chen, Liang Hong, Xinyuan Liu +6
Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies. Conventional RNA foundation models couple parameter capacity…
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12
Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this v…
EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
Chao Song, Zhiyuan Liu, Han Huang +6
Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but…
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
Zhiyuan Liu, Yanchen Luo, Han Huang +7
3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers,…
RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models
Han Huang, Ziqian Lin, Dongchen He +2
RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find f…
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Han Huang, Leilei Sun, Bowen Du +1
Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress…
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
Mingzhe Liu, Han Huang, Hao Feng +3
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in…
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Han Huang, Leilei Sun, Bowen Du +1
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, acc…
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation
Han Huang, Leilei Sun, Bowen Du +2
Graph generative models have broad applications in biology, chemistry and social science. However, modelling and understanding the generative process of graphs is challenging due t…