Related Experiment Video
Updated: Sep 18, 2025
05:08
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
374
Pre-trained language models for protein and molecular design.
Erdong Zhang1, Zilin Pan1, Zequan Yao1
1Intelligent Medical Research Center, School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China. zhangerd@mail2.sysu.edu.cn.
Physical Chemistry Chemical Physics : PCCP
|June 27, 2025
Summary
Pre-trained language models (PLMs) are revolutionizing biological research by applying natural language processing techniques to biological sequences. These models show great promise for protein research and drug design applications.
Area of Science:
- Computational Biology
- Bioinformatics
- Machine Learning in Biology
Background:
- Pre-trained language models (PLMs) demonstrate significant capabilities in natural language understanding.
- Biological sequences share structural similarities with natural language, enabling PLM application in life sciences.
Purpose of the Study:
- To summarize the expanding field of PLMs in biological research.
- To highlight exemplary PLMs, datasets, and their applications in prediction and generation tasks.
Main Methods:
- Leveraging the structural parallels between biological sequences and natural language.
- Pre-training PLMs on large unlabeled biological sequence datasets.
- Fine-tuning PLMs for specific biological research tasks.
Main Results:
- PLMs exhibit exceptional performance in biological research applications.
- Successful application of PLMs in protein research and drug design.
- Demonstrated potential in both predictive and generative biological tasks.
Conclusions:
- PLMs offer powerful new solutions for biological research challenges.
- The integration of PLMs with biological data is a rapidly growing area.
- PLMs hold significant promise for future advancements in drug discovery and protein engineering.
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