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20222026
most citedAdvances of Deep Learning in Protein Science: A Comprehensive Survey

7 citations · 12 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

E-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation

Yifei Liu, Minghao Fang, Xinyu Gu +6

On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solution and as student with only t…

cs.LG2026

Bison: Cross-Dataset Learning for Unseen-Compound Perturbation Prediction

Yunfan Liu, Kasra Ghorbani, Yufei Huang +6

Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene panels. To assess molecular ge…

cs.LG2025

Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges

Changxi Chi, Yufei Huang, Jun Xia +4

Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translation…

cs.LG2025★ 1 cited

Multimodal Regression for Enzyme Turnover Rates Prediction

Bozhen Hu, Cheng Tan, Siyuan Li +4

The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most org…

cs.LG2024

Graph-level Protein Representation Learning by Structure Knowledge Refinement

Ge Wang, Zelin Zang, Jiangbin Zheng +2

This paper focuses on learning representation on the whole graph level in an unsupervised manner. Learning graph-level representation plays an important role in a variety of real-w…

cs.LG2023★ 1 cited

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Yufei Huang, Siyuan Li, Jin Su +9

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimati…

cs.LG2023★ 1 cited

Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning

Zihan Liu, Ge Wang, Jiaqi Wang +2

Peptides are formed by the dehydration condensation of multiple amino acids. The primary structure of a peptide can be represented either as an amino acid sequence or as a molecula…