collaborators

7 papers

q-bio.QM2026

EasyNano: rapid epitope-targeted nanobody CDR design via differentiable distogram optimization with ESMFold2

Yue Hu, Wanyu Cheng, Junqing Wang +1

Computational design of nanobodies that bind user-specified protein epitopes could transform therapeutic development, but current methods either rely on stochastic sampling requiri…

q-bio.QM2026

Interpretable enzyme function prediction via sparse autoencoder features of ESMC across the microbial protein universe

Yue Hu, Wanyu Cheng, Junqing Wang +1

Microbial genomes and metagenomes contain millions of proteins whose enzymatic functions remain unknown, the enzyme dark matter. While deep learning has improved protein function p…

q-bio.QM2026

TurboESM: Ultra-Efficient 3-Bit KV Cache Quantization for Protein Language Models with Orthogonal Rotation and QJL Correction

Yue Hu, Junqing Wang, Yingchao Liu

The rapid scaling of Protein Language Models (PLMs) has unlocked unprecedented accuracy in protein structure prediction and design, but the quadratic memory growth of the Key-Value…

q-bio.QM2026

AntibodyDesignBFN: High-Fidelity Fixed-Backbone Antibody Design via Discrete Bayesian Flow Networks

Yue Hu, Feng Tao, Junqing Wang +1

The computational design of antibodies with high specificity and affinity is a cornerstone of modern therapeutic development. While deep generative models have demonstrated potenti…

q-bio.QM2025

UniOTalign: A Global Matching Framework for Protein Alignment via Optimal Transport

Yue Hu, Zanxia Cao, Yingchao Liu

Protein sequence alignment is a cornerstone of bioinformatics, traditionally approached using dynamic programming (DP) algorithms that find an optimal sequential path. This paper i…

q-bio.QM2025

Lie-RMSD: A Gradient-Based Framework for Protein Structural Alignment using Lie Algebra

Yue Hu, Zanxia Cao, Yingchao Liu

The comparison of protein structures is a fundamental task in computational biology, crucial for understanding protein function, evolution, and for drug design. While analytical me…