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Chai 1
Overview :
Chai-1 is a multimodal foundational model for drug discovery that predicts the molecular structures of proteins, small molecules, DNA, RNA, and covalent modifications. It achieved a 77% success rate in the PoseBusters benchmark tests, comparable to AlphaFold3. Chai-1 operates without requiring multiple sequence alignments while maintaining most of its performance and can accurately fold polymeric structures. Additionally, Chai-1 can enhance predictive performance by incorporating laboratory data. This model aims to transform biology from a science into an engineering discipline, promoting the application of AI in biological research.
Target Users :
Chai-1 is suitable for researchers and companies in the drug discovery field because it provides an advanced tool for predicting and analyzing molecular structures, thereby accelerating the drug design and development process.
Use Cases
Used for predicting molecular structures of new drugs, accelerating the drug screening process.
In the laboratory, it improves the accuracy of antibody-antigen structure prediction by integrating experimental data.
Used in drug design to optimize molecular binding sites, enhancing drug efficacy and safety.
Features
Unified prediction of molecular structures for proteins, small molecules, DNA, RNA, etc.
Operates without multiple sequence alignments while maintaining most of its performance.
Predicts polymeric structures in single-sequence mode, achieving quality comparable to AlphaFold-Multimer.
Enhances predictive performance through experimental data inputs.
Freely available via a web interface, including for commercial applications.
Model weights and inference code released as a software library for non-commercial use.
How to Use
Access the Chai-1 web interface: https://lab.chaidiscovery.com.
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Register and log in to gain access to the model.
Upload the molecular sequences or structural data for prediction.
Select the prediction mode, such as single-sequence or multiple-sequence alignment.
If necessary, input laboratory data or experimental constraints.
Submit the prediction request and wait for the model to process it.
View and analyze the prediction results, and conduct further experimental validation as needed.
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