Author

Yunzhi Lang

Changping LaboratoryORCID 0009-0004-8136-323XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:ChIP-seq· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1 Research Focus:protein design· 1 Assay:protein structure· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Mingchen CheniD· 2 shared Baihui TianiD· 1 shared Chen Xie· 1 shared Di Wang· 1 shared Di WangiD· 1 shared Dubai LiiD· 1 shared Hongzhun WangiD· 1 shared Jian YaniD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBiophysics

Mingchen ChenORCID

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2 papers

Recent·Most cited

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Zi Wang, Di Wang, Ke Shen, Junchen Luo, Xinyao Wang, Nan Wu, Yunzhi Lang, X Wang, Jun Ren, Wenyang Dong, Lu Pan, Yitong Lyu, Dubai Li, Chen Xie, Zhen Zhang, , Xiaoliang Sunney Xie

The paper introduces Seq2Kd, a deep learning model predicting TF-DNA binding affinity using high-throughput Kd measurements, and provides a web portal for functional genomics and disease research.

bioRxiv · May 20, 2026 · PreprintBiophysics

High-Affinity Protein Binder Design via Flow Matching and In Silico Maturation

Qilin Yu, Liangyue Guo, Xiayan Qin, Xikun Huang, Baihui Tian, Hongzhun Wang, Yu Liu, Yunzhi Lang, Di Wang, Zhongkai Shen, Jie Lin, Mingchen Chen

This paper designs high-affinity protein binders using a computational framework combining flow matching and in silico maturation, achieving picomolar and nanomolar affinities without experimental screening.

bioRxiv · Jan 23, 2026 · PreprintBiochemistry