5 papers
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…
Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering
Chen Liu, Mingchen Li, Yang Tan +3
A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants…
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts
Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well ch…
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Liang Zhang, Hua Pang, Chenghao Zhang +11
In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (…