OpenClaw Scientific Research

🔬 OpenClaw Scientific Skill

A comprehensive AI-powered scientific research toolkit for OpenClaw

Transform your AI assistant into a scientific research powerhouse

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✨ Features

🧬 Bioinformatics & Genomics

• Sequence analysis with BioPython
• Single-cell RNA-seq with Scanpy
• Variant annotation and interpretation
• Phylogenetic analysis

🧪 Cheminformatics & Drug Discovery

• Molecular manipulation with RDKit
• Virtual screening and ADMET prediction
• Molecular docking workflows
• Chemical database access (ChEMBL, PubChem, ZINC)

🏥 Clinical Research & Precision Medicine

• ClinicalTrials.gov integration
• Variant interpretation (ClinVar, COSMIC)
• Pharmacogenomics workflows
• Cancer genomics (cBioPortal, DepMap)

🤖 Machine Learning & AI

• Deep learning with PyTorch Lightning
• Time series forecasting with TimesFM
• Bayesian methods with PyMC
• Model interpretability with SHAP

📚 250+ Scientific Databases

• Literature: PubMed, OpenAlex, bioRxiv
• Proteins: UniProt, PDB, AlphaFold DB
• Chemicals: ChEMBL, PubChem, DrugBank
• Clinical: ClinVar, COSMIC, ClinicalTrials.gov

📊 Scientific Visualization & Publishing

• Publication-quality figures
• LaTeX document generation
• Presentation slides
• Citation management

⚛️ Physics & Quantum Computing

• Astronomical data analysis (Astropy)
• Quantum computing (Qiskit, PennyLane)
• Symbolic mathematics (SymPy)

⚙️ Engineering & Optimization

• Multi-objective optimization (PyMOO)
• Metabolic modeling (COBRApy)
• Materials science (Pymatgen)

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🚀 Quick Start

Installation

# The skill is automatically loaded by OpenClaw
# Just copy to your skills directory
cp -r scientific ~/.openclaw/skills/

Example Usage

Single-cell RNA-seq Analysis:

import scanpy as sc

# Load and analyze
adata = sc.read_10x_mtx('./data/')
sc.pp.filter_cells(adata, min_genes=200)
sc.tl.pca(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
sc.pl.umap(adata, color='leiden')

Query PubMed:

from Bio import Entrez
Entrez.email = "[email protected]"

handle = Entrez.esearch(db="pubmed", term="EGFR lung cancer", retmax=20)
results = Entrez.read(handle)

Fetch AlphaFold Structure:

python scripts/fetch_alphafold.py P00533 --output EGFR.pdb

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📁 Structure

scientific/
├── SKILL.md                    # Main skill definition
├── references/
│   ├── bioinformatics.md       # Genomics, single-cell, phylogenetics
│   ├── cheminformatics.md      # Drug discovery, molecular modeling
│   ├── clinical.md             # Clinical trials, variant interpretation
│   ├── ml.md                   # Deep learning, time series, Bayesian
│   ├── databases.md            # 250+ database access patterns
│   ├── documents.md            # Visualization, LaTeX, publishing
│   ├── physics.md              # Astronomy, quantum computing
│   └── engineering.md          # Optimization, metabolic modeling
└── scripts/
    ├── install_deps.py         # Install domain-specific dependencies
    ├── query_pubmed.py         # PubMed search utility
    └── fetch_alphafold.py      # AlphaFold structure downloader

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🔧 Dependencies

Install dependencies by domain:

# Core scientific stack
pip install numpy scipy pandas matplotlib seaborn

# Bioinformatics
pip install biopython scanpy pysam

# Cheminformatics
pip install rdkit deepchem

# Machine Learning
pip install torch pytorch-lightning scikit-learn

# Or use the provided script
python scripts/install_deps.py --domain bioinformatics
python scripts/install_deps.py --domain ml
python scripts/install_deps.py --domain all

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🗃️ Database Coverage

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📖 Documentation

• Bioinformatics Reference - Detailed genomics workflows
• Cheminformatics Reference - Drug discovery pipelines
• Clinical Reference - Precision medicine workflows
• ML Reference - Machine learning guides
• Databases Reference - API patterns for 250+ databases

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🤝 Comparison with Claude Scientific Skills

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📝 Example Workflows

Find EGFR Inhibitors

Query ChEMBL for EGFR inhibitors (IC50 < 50nM),
analyze SAR with RDKit, perform virtual screening

Single-cell Analysis

Load 10X data with Scanpy, QC filtering,
PCA/UMAP, Leiden clustering, marker identification

Variant Interpretation

Parse VCF, annotate with Ensembl VEP,
query ClinVar for pathogenicity,
check COSMIC for cancer mutations

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📜 License

MIT License - Free for academic and commercial use.

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🙏 Acknowledgments

Inspired by Claude Scientific Skills by K-Dense.

This is an independent implementation with original code examples. See ACKNOWLEDGMENTS.md for details.

Built on amazing open-source projects:

• BioPython
• Scanpy
• RDKit
• PyTorch
• scikit-learn

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Made with ❤️ for the scientific community

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