DescipherLab

Decipher the Code of Life: AI-Powered Protein Structure Prediction

Step into the future of protein science, where Artificial Intelligence meets DeScience to transform our understanding of life's most intricate systems. At Descipher, 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

Global research partners

LIVE: Unveiling New Protein Structures /

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

Open Science Platform

Current Completed Predictions:

Scientists Involved Worldwide:

Number Of Institutions:

162,953

Protein Models

3,245

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.