16 citations · 60 across the 15 of their papers we have counts for
18 papers
VenusGenome: Genome-Aware Masked Autoencoding for Microbial Genome Representation Learning
Zhang Liang, Zhang Yukun, Tan Yang +6
Genomic-context models have shown that contextualizing protein embeddings can improve predictions of microbial function. We observe that a gene's protein embedding is partially pre…
PROTEAN: Beyond De Novo Generation - Can LLM Agents Perform Text-Guided Protein Structure Editing?
Encheng Su, Xinping Liu, Yizhou Wang +11
Generative AI has achieved remarkable success in de novo protein design, yet protein engineering typically begins with existing, characterized proteins. The primary demand is targe…
A General Harness for Protein Foundation Model Fitness Prediction
Yang Tan, Qijia Tian, Gangyu Sun +5
Accurate fitness prediction is central to protein engineering and understanding sequence-function relationships. With advances in deep learning, protein foundation models (PFMs) ha…
Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
Chen Tang, Yizhou Wang, Jianyu Wu +26
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and pe…
Self-evolving AI agents for protein discovery and directed evolution
Yang Tan, Lingrong Zhang, Mingchen Li +5
Protein scientific discovery is bottlenecked by the manual orchestration of information and algorithms, while general agents are insufficient in complex domain projects. VenusFacto…
Rank-and-Reason: Multi-Agent Collaboration Accelerates Zero-Shot Protein Mutation Prediction
Yang Tan, Yuanxi Yu, Can Wu +7
Zero-shot mutation prediction is vital for low-resource protein engineering, yet existing protein language models (PLMs) often yield statistically confident results that ignore fun…
VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
Yang Tan, Wenrui Gou, Bozitao Zhong +3
Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many b…
ReactZyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua, Bozitao Zhong, Sitao Luan +4
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations. Predicting enzyme functions is essen…
Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion
Dingyi Rong, Wenzhuo Zheng, Bozitao Zhong +3
Accurate prediction of enzyme function is crucial for elucidating biological mechanisms and driving innovation across various sectors. Existing deep learning methods tend to rely s…
Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?
Yang Tan, Lirong Zheng, Bozitao Zhong +2
Deep learning has become a crucial tool in studying proteins. While the significance of modeling protein structure has been discussed extensively in the literature, amino acid type…
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
Yang Tan, Mingchen Li, Bingxin Zhou +7
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised lea…
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
Jiarui Lu, Zuobai Zhang, Bozitao Zhong +2
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations…
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang, Bozitao Zhong, Zuobai Zhang +3
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a universally accepted method for evaluation has…
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
Jiarui Lu, Bozitao Zhong, Zuobai Zhang +1
The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as pre…
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yangtian Zhang, Zuobai Zhang, Bozitao Zhong +2
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Accurately predicting the conformation of…
Protein Sequence and Structure Co-Design with Equivariant Translation
Chence Shi, Chuanrui Wang, Jiarui Lu +2
Proteins are macromolecules that perform essential functions in all living organisms. Designing novel proteins with specific structures and desired functions has been a long-standi…
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Yangtian Zhang, Huiyu Cai, Chence Shi +2
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery. This work focuses on blind flexible selfdocking, wher…
ParaFold: Paralleling AlphaFold for Large-Scale Predictions
Bozitao Zhong, Xiaoming Su, Minhua Wen +3
AlphaFold predicts protein structures from the amino acid sequence at or near experimental resolution, solving the 50-year-old protein folding challenge, leading to progress by tra…