Predict protein, nucleic-acid, and small-molecule complex structures with Chai-1.
npx skills add https://github.com/guanxiaol/open-science --skill chai1
Please help me install this Agent Skill. Skill: chai1 Source: https://github.com/guanxiaol/open-science/tree/main/runtime/skills/chai1 Command: npx skills add https://github.com/guanxiaol/open-science --skill chai1
This Skill addresses the challenge of predicting protein, nucleic-acid, and small-molecule complex structures, providing a comprehensive solution for structure prediction tasks.
Run the Chai-1 skill to predict the structure of a protein complex by providing a FASTA file and specifying the output directory.
High-intent search queries and answers about installing and using this skill.
To predict protein complex structures, provide a FASTA file as input and specify an output directory. The Chai-1 foundation model processes the sequences to generate the predicted complex structures.
Yes, Chai-1 supports predicting antibody-antigen and protein-ligand complexes. It handles protein, nucleic-acid, and small-molecule complex structure predictions from a single FASTA file.
Yes, Chai-1 can be used for re-folding designed binders as an alternative to AlphaFold-multimer. It utilizes its foundation model to predict the folded complex structures.
Yes, a GPU is required to run Chai-1 for molecular structure prediction. The Chai-1 foundation model relies on GPU acceleration to process and predict complex structures.
You can drive co-folding from Python to execute batched campaigns on a GPU. This allows automated processing of multiple protein, nucleic-acid, and small-molecule complex predictions.
Chai-1 predicts structures for complexes involving proteins, nucleic acids, and small molecules. It is suitable for antibody-antigen, protein-ligand, and co-folding tasks.
PKU-YuanGroupPredict protein, nucleic-acid, and small-molecule complex structures with Chai-1.
Runchuan-BUQuery the AlphaFold EBI API for UniProt protein structures and retrieve PDB/CIF files and confidence metrics.
PKU-YuanGroupPredict protein, nucleic-acid, and small-molecule complex structures with Boltz-2.
PharMolixPredict antibody and nanobody structures from sequences using tfold-based models.
