5 citations · 10 across the 8 of their papers we have counts for
8 papers
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab
Zan Chen, Yungeng Liu, Yu Guang Wang +1
Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry…
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
Yungeng Liu, Zan Chen, Yu Guang Wang +1
The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgen…
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
Yungeng Liu, Zan Chen, Yu Guang Wang +1
Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown p…
A Regressor-Guided Graph Diffusion Model for Predicting Enzyme Mutations to Enhance Turnover Number
Xiaozhu Yu, Kai Yi, Yu Guang Wang +1
Enzymes are biological catalysts that can accelerate chemical reactions compared to uncatalyzed reactions in aqueous environments. Their catalytic efficiency is quantified by the t…
LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble
Taoyu Wu, Yu Guang Wang, Yiqing Shen
Protein inverse folding aims to identify viable amino acid sequences that can fold into given protein structures, enabling the design of novel proteins with desired functions for a…
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
Yiqing Shen, Zan Chen, Michail Mamalakis +6
The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLM…
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding
Yiqing Shen, Zan Chen, Michail Mamalakis +6
The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding.…
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering
Yiqing Shen, Outongyi Lv, Houying Zhu +1
Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-c…