NeoFold: Reshaping Protein Science with AI and DeSci.
Step into the future of protein science, where Artificial Intelligence meets DeScience to transform our understanding of life's most intricate systems. At NeoFold, we're not just predicting protein structures—we're redefining how they shape biomedicine, drug discovery, and the frontiers of molecular innovation.
15+
AI-driven models developed
150,000+
Proteins analyzed
87
Global research partners
Exploring the Unknown: Breakthrough Approaches to Protein Prediction
Our work blends six pioneering methodologies, each engineered to tackle the toughest challenges in protein structure prediction. By merging AI-driven insights with experimental data, we're charting unexplored territory in molecular biology, pushing the boundaries of what's possible.
Assembly Modeling
Template-Based Modeling
Ab Initio Modeling
Contact Prediction
Refinement
Data-Assisted Modeling
AI and DeScience: A Unified Framework for Protein Research
AI-Driven Protein Research
Deep learning techniques
Generative AI models
Multimodal data integration
DeScience: Decentralized Collaboration
Decentralized
Collaboration & Validation
Collaboration & Validation
Open Science Platform
Current Completed Predictions:
Scientists Involved Worldwide:
Number Of Institutions:
162,953
Protein Models
3,245
87
Empowering Discovery: Data and Collaboration at Your Fingertips
Data Resources
Protein Sequence Database
Experimental data
Predicted 3D structure data
Researcher Community
Cases
Development of a Novel Anti-Viral Protein through Assembly Modeling
Cooperating Agencies
- Harvard Medical School, USA
- University of Oxford, UK
- Kyoto University, Japan
Achieve results
- Successfully predicted the 3D structure of an antiviral protein complex and published it to a public database.
- The research results are cited in the latest research on antiviral drug development.
High-Precision Refinement of Vaccine Target Proteins
Cooperating Agencies
- Max Planck Institute for Biophysical Chemistry, Germany
- Tsinghua University, China
Achieve results
- Refined the initial structure of a vaccine candidate protein, increasing the LDDT score from 75% to 92%
- The refined model provides a more reliable target structure for vaccine development.
Contact Prediction for Understanding Pathogenic Mechanisms
Cooperating Agencies
- Sapienza University of Rome, Italy
- Cairo University, Egypt
Achieve results
- Key interaction sites that accurately identify a particular pathogen protein through contact prediction.
- The results are used to design specific molecular inhibitors.
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