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HPC-scale literature mining and retrieval-augmented generation for structural biology
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Based on SOC occupation classification
Search and select enzyme sequence homologs from a database. Use when users provide a query sequence and want to discover sequence homologs of the query sequence in a database.
Scientific reasoning via Jnana CoScientist — hypothesis generation, evaluation, and parameter bounding
Molecular dynamics simulations using OpenMM with MDAgent-style automation and free energy calculations via MM-PBSA
Convert scientific papers into executable computational workflows and MCP tools
Protein language model embeddings, diversity sampling, and mutation prediction using ESM or GenSLM
Protein structure prediction using Chai-1 or AlphaFold with confidence scoring, quality assessment, and critic evaluation
| name | hiperrag |
| description | HPC-scale literature mining and retrieval-augmented generation for structural biology |
| metadata | {"openclaw":{"requires":{"env":["OPENAI_API_KEY"],"bins":["python3"]},"primaryEnv":"OPENAI_API_KEY"}} |
Search and synthesize structural biology literature at scale using HiPerRAG.
interactome: Identify interacting proteins, pathways, and therapeutic rationales for a target proteinbinder_design: Find starting binder sequences and scaffolds for BindCraft optimizationpython skills/hiperrag/scripts/run_rag.py \
--query "Identify interacting proteins for KRAS" \
--target-protein KRAS \
--prompt-type interactome \
--output-dir ./artifacts
query (required): Research question or protein targettarget_protein (required): Target protein name (e.g., KRAS, TP53)prompt_type: Prompt mode — interactome or binder_design (default: interactome)generator: LLM backend — openai, vllm, or argo (default: openai)model: Model name for the generator (default: gpt-4o)retrieval_top_k: Number of documents to retrieve (default: 200)output_dir: Directory for artifact storage (default: ./artifacts)rag_config: Path to a YAML/JSON RAG config file (optional, overrides CLI flags)