22 citations · 27 across the 4 of their papers we have counts for
7 papers
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
Zhenqiao Song, Yunlong Zhao, Wenxian Shi +3
Enzymes are genetically encoded biocatalysts capable of accelerating chemical reactions. How can we automatically design functional enzymes? In this paper, we propose EnzyGen, an a…
Joint Design of Protein Sequence and Structure based on Motifs
Zhenqiao Song, Yunlong Zhao, Yufei Song +3
Designing novel proteins with desired functions is crucial in biology and chemistry. However, most existing work focus on protein sequence design, leaving protein sequence and stru…
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design
Zhenqiao Song, Yunlong Zhao, Wenxian Shi +2
Proteins are macromolecules responsible for essential functions in almost all living organisms. Designing reasonable proteins with desired functions is crucial. A protein's sequenc…
Follow Your Path: a Progressive Method for Knowledge Distillation
Wenxian Shi, Yuxuan Song, Hao Zhou +2
Deep neural networks often have a huge number of parameters, which posts challenges in deployment in application scenarios with limited memory and computation capacity. Knowledge d…
Variational Template Machine for Data-to-Text Generation
Rong Ye, Wenxian Shi, Hao Zhou +2
How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an…
Kernelized Bayesian Softmax for Text Generation
Ning Miao, Hao Zhou, Chengqi Zhao +2
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase. Most existing approaches adopt single point embedding for each tok…