chai
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill chai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Chai-1 Structure Prediction ## Prerequisites | Requirement | Minimum | Recommended | |-------------|---------|-------------| | Python | 3.10+ | 3.11 | | CUDA | 12.0+ | 12.1+ | | GPU VRAM | 24GB | 40GB (A100) | | RAM | 32GB | 64GB | ## How to run > **First time?** See [Installation Guide](../../docs/installation.md) to set up Modal and biomodals. ### Option 1: Modal ```bash cd biomodals modal run modal_chai1.py \ --input-faa complex.fasta \ --out-dir predictions/ ``` **GPU**: A100 (40GB) | **Timeout**: 30min default ### Option 2: Chai API (recommended) ```bash pip install chai_lab python -c " import chai_lab from chai_lab.chai1 import run_inference # Run prediction run_inference( fasta_file='complex.fasta', output_dir='predictions/', num_trunk_recycles=3 ) " ``` ### Option 3: Local installation ```bash git clone https://github.com/chaidiscovery/chai-lab.git cd chai-lab pip install -e . chai-lab predict \ --fasta complex.fasta \ --output predictions/ ``` ## FASTA Format ### Protein complex ``` >binder MKTAYIAKQRQISFVKSHFSRQLE... >target MVLSPADKTNVKAAWGKVGAHAGE... ``` ### Protein + ligand ``` >protein MKTAYIAKQRQISFVKSHFSRQLE... >ligand|smiles CCO ``` ### Protein + DNA/RNA ``` >pro
- Prerequisites
- How to run
- Option 1: Modal
- Option 2: Chai API (recommended)
- Option 3: Local installation
- FASTA Format
- Protein complex
- Protein + ligand
- Protein + DNA/RNA
- Key parameters
- Output format
- Extracting metrics
- Use cases
- Binder validation
cd biomodals modal run modal_chai1.py \ pip install chai_lab python -c " import chai_lab from chai_lab.chai1 import run_inference Run prediction git clone https://github.com/chaidiscovery/chai-lab.git cd chai-lab pip install -e .
What does the chai skill do?
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For ESM-based analysis, use esm.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill chai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.