OpenClaw Medical Skills
The largest open-source medical AI skill library for OpenClaw.
869 curated skills · Clinical · Genomics · Drug Discovery · Bioinformatics · Medical Devices
What Is This?
OpenClaw Medical Skills is a curated collection of 869 AI agent skills covering the full spectrum of biomedical and clinical research. These skills are designed for OpenClaw / NanoClaw — Claude-based personal AI assistant frameworks — and transform a general-purpose AI agent into a powerful medical and scientific research companion.
Each skill is a self-contained module (a SKILL.md file) that:
- Teaches the agent specialized domain knowledge and workflows
- Connects to real databases, APIs, and computational tools
- Produces structured, clinically or scientifically relevant outputs
We benefit from the open-source community. The full collection of resources can be found here: Awesome LLM Resources
Why This Collection Matters
| Without Skills | With OpenClaw Medical Skills |
|---|---|
| Generic AI responses about medicine | Real PubMed / ClinicalTrials.gov / FDA queries |
| No bioinformatics capability | RNA-seq, scRNA-seq, GWAS, variant calling pipelines |
| No drug intelligence | ChEMBL, DrugBank, DDI prediction, pharmacovigilance |
| No clinical documentation | SOAP notes, discharge summaries, prior auth decisions |
| No genomics support | VCF annotation, ACMG classification, PRS calculation |
| No regulatory guidance | FDA, CE mark, IEC 62304, ISO 14971 compliance |
This collection aggregates skills from 12+ open-source skill repositories spanning academic research tools, clinical workflows, regulatory frameworks, and cutting-edge AI-driven protein design — giving your AI agent capabilities comparable to a team of specialized research scientists.
Installation
Requirements
For OpenClaw Users
OpenClaw loads skills from two locations:
| Priority | Path | Scope |
|---|---|---|
| High | <workspace>/skills/ | Per-workspace (recommended) |
| Low | ~/.openclaw/skills/ | Global, shared across all agents |
Method 1 — Clone and Copy (Recommended)
# Clone this repository (skills only — skips large data files)
git clone --depth=1 --no-checkout https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git
cd OpenClaw-Medical-Skills
git sparse-checkout init --cone
git sparse-checkout set skills
git checkout main
# Install to your workspace skills directory
cp -r skills/* <your-workspace>/skills/
# Or install globally (available to all agents)
cp -r skills/* ~/.openclaw/skills/
Note: Some skills bundle large data files (databases, datasets). The sparse-checkout method above avoids downloading them. If you need the full repo including all data, install Git LFS first, then run
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git.
Skills are picked up automatically on the next session. No restart needed.
Method 2 — OpenClaw CLI
If you use the OpenClaw plugin registry, you can search and install individual skills from there. For bulk install from this collection, Method 1 is faster.
openclaw plugins install <skill-slug> # install a single skill
openclaw plugins update # update all installed skills
Method 3 — Configure Extra Directories
To point OpenClaw at a cloned copy of this repo permanently, add it to ~/.openclaw/openclaw.json:
{
"plugins": {
"local": ["/path/to/OpenClaw-Medical-Skills"]
}
}
This mounts the entire collection without copying files.
Method 4 — Install Selected Skills Only
Pick skills relevant to your domain:
# Example: clinical + drug discovery stack
SKILLS=(
"clinical-reports"
"tooluniverse-drug-research"
"tooluniverse-pharmacovigilance"
"clinicaltrials-database"
"biomedical-search"
"tooluniverse-drug-drug-interaction"
)
for skill in "${SKILLS[@]}"; do
cp -r OpenClaw-Medical-Skills/skills/$skill ~/.openclaw/skills/
done
For NanoClaw Users
NanoClaw loads skills into agent containers at startup from container/skills/.
# Clone and copy into NanoClaw container skills directory
git clone https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills.git
cp -r OpenClaw-Medical-Skills/skills/* /path/to/nanoclaw/container/skills/
# Rebuild the container to apply
cd /path/to/nanoclaw
./container/build.sh
Verification
After installation, ask your agent:
What medical and clinical skills do you have available?
Your agent should list the installed skills with their capabilities.
Skills Overview
| Category | Count | Highlights |
|---|---|---|
| General & Core | 10 | Browser/search, document tools, and developer workflow utilities |
| Medical & Clinical | 119 | Clinical reports, CDS, oncology, imaging, and healthcare AI |
| Scientific Databases | 43 | Genomics/protein/drug databases and biomedical knowledge retrieval |
| Bioinformatics (gptomics) | 239 | Variant analysis, sequencing QC, DE, pathways, single-cell, and epigenomics |
| Omics & Computational Biology | 59 | Single-cell/spatial, proteomics, cheminformatics, and protein design tools |
| ClawBio Pipelines | 21 | Orchestration pipelines for scRNA, GWAS, ancestry, and structural workflows |
| BioOS Extended Suite | 285 | Extended agent suite for oncology, immunology, clinical AI, and infrastructure |
| Data Science & Tools | 93 | Statistics, visualization, automation, simulation, and scientific tooling |
| Total | 869 |
Table of Contents
General & Core
Medical & Clinical
- Medical Tools
- Drug Safety & Chemical Biology
- Medical Imaging & Pathology
- Healthcare ML & Clinical AI
- Mental Health & Crisis Intervention
- Health & Wellness Analytics
- Medical Device & Regulatory
- Medical Device Software (meddev-agent-skills)
Scientific Databases
- Scientific Databases (Genomics & Variants)
- Scientific Databases (Proteins, Pathways & Drugs)
- Cancer Genomics Databases
- Genomic & Molecular Databases
- Structural Biology & Drug Discovery
Bioinformatics (gptomics bio-* suite)
- Bioinformatics Tools & Pipelines
- Bioinformatics — Clinical Databases & Variant Analysis
- Bioinformatics — Sequencing & Read QC
- Bioinformatics — Differential Expression & Transcriptomics
- Bioinformatics — Pathway & Network Analysis
- Bioinformatics — Single-Cell & Spatial Omics
- Bioinformatics — Epigenomics & Chromatin
- Bioinformatics — Metagenomics & Microbiome
- Bioinformatics — Immunoinformatics & Flow Cytometry
- Bioinformatics — Multi-Omics Integration
- Bioinformatics — Proteomics & Metabolomics
- Bioinformatics — Structural Biology & Cheminformatics
- Bioinformatics — Epidemiological & Causal Genomics
Omics & Computational Biology
- Single-Cell & Spatial Omics
- Single-Cell & Trajectory Analysis
- Proteomics & Mass Spectrometry
- Cheminformatics & Drug Discovery
- Protein Structure & Design
- Phylogenetics & Network Analysis
ClawBio Pipelines
- Bioinformatics Orchestration & Pipelines (ClawBio)
- Genomics, Ancestry & Pharmacogenomics (ClawBio)
- Structural Biology & Literature (ClawBio)
BioOS Extended Suite
- BioOS Extended Bioinformatics Suite
- Oncology & Precision Medicine Agents (BioOS)
- Hematology & Blood Disorders (BioOS)
- Immunology & Cell Therapy (BioOS)
- Single-Cell & Spatial Agents (BioOS)
- Drug Discovery & Design (BioOS)
- Clinical AI & Healthcare (BioOS)
- Research Infrastructure & Agents (BioOS)
Data Science & Tools
- Statistics & Data Analysis
- Data Processing & Scientific Computing
- Scientific Visualization & Communication
- Public Health & Time Series
- Computational Simulation & Ontology
- Analyst Personas
- Lab Automation & Integration
- Scientific Research & Writing
- Scientific Literature & Reference Management
- Additional Scientific Tools
- Developer Workflow Skills
Skills List
🧰 General & Core
Expand/Collapse this category
General Tools
Click to expand skill list
| Skill | Description |
|---|---|
| agent-browser | Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful. |
| find-skills | Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. |
| multi-search-engine | Multi search engine integration with 17 engines (8 CN + 9 Global). Supports Baidu, Bing, 360, Sogou, WeChat, Google, DuckDuckGo, WolframAlpha and more. Supports advanced operators, time filters, site search. No API keys required. |
| wikipedia-search | Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information. Supports multi-language queries. |
| deep-research | Execute autonomous multi-step deep research on any topic. Searches multiple sources, reads full content, synthesizes findings, and produces a structured report. Use for comprehensive research, literature reviews, competitive analysis, or topic deep-dives. |
| Comprehensive PDF toolkit — extract text and tables, create new PDFs, merge/split documents, handle forms, OCR scanned PDFs. Use when working with any .pdf file. | |
| docx | Create, edit, and analyze Word documents (.docx). Supports tracked changes, comments, formatting preservation, and text extraction. Use for drafting, redlining, or extracting content from Word files. |
| xlsx | Spreadsheet creation, editing, and analysis. Supports formulas, formatting, data analysis, and visualization. Use for any .xlsx, .xlsm, .csv, or .tsv task. |
| pptx | Presentation creation, editing, and analysis. Supports layouts, speaker notes, templates, and design. Use for any .pptx file. |
| doc-coauthoring | Guide users through a structured workflow for co-authoring documentation. Use when writing documentation, proposals, technical specs, decision docs, or similar structured content. |
🏥 Medical & Clinical
Expand/Collapse this category
Medical Tools
Click to expand skill list
| Skill | Description |
|---|---|
| pubmed-search | Search PubMed for scientific literature. Use when the user asks to find papers, search literature, look up research, find publications, or asks about recent studies. |
| medical-research-toolkit | Query 14+ biomedical databases for drug repurposing, target discovery, clinical trials, and literature research. Access ChEMBL, PubMed, ClinicalTrials.gov, OpenTargets, OpenFDA, OMIM, Reactome, KEGG, UniProt, and more through a unified MCP endpoint. |
| medical-specialty-briefs | Generate daily or on-demand medical research briefs for any medical specialty. Searches latest research from top-tier journals (NEJM, Lancet, JAMA, BMJ, Nature Medicine), delivers concise summaries with 1-sentence takeaways and direct links. Use when user asks for medical news, research updates, or specialty-specific updates (endocrinology, cardiology, oncology, neurology, etc.). |
| usmle | Prepare for US medical licensing exams with progress tracking, weak area analysis, question bank management, and residency match planning. Covers Step 1/2 CK/Step 3, IMG-specific guidance, score prediction, and wellbeing support. |
| medical-entity-extractor | Extract medical entities (symptoms, medications, lab values, diagnoses) from patient messages. |
| patiently-ai | Simplifies medical documents for patients. Takes doctor's letters, test results, prescriptions, discharge summaries, and clinical notes and explains them in clear, personalised language. |
| biomedical-search | Complete biomedical information search combining PubMed, preprints, clinical trials, and FDA drug labels. Powered by Valyu semantic search. |
| medical-imaging-review | Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on imaging topics. |
| fhir-developer-skill | FHIR API development guide for building healthcare endpoints (Patient, Observation, Encounter, Condition, MedicationRequest). Use when developing or integrating FHIR REST APIs. |
| clinical-trial-protocol-skill | Generate clinical trial protocols for medical devices or drugs. Use when designing clinical studies, creating FDA submission documentation, or developing protocols for investigational products. |
| prior-auth-review-skill | Automate payer review of prior authorization (PA) requests. Assesses medical necessity, validates against coverage policies, and generates PA decisions. |
| clinical-reports | Write comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, CSR), and patient documentation (SOAP, H&P, discharge summaries). HIPAA/FDA/ICH-GCP compliant. |
| clinicaltrials-database | Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data for clinical research and patient matching. |
| clinical-decision-support | Generate clinical decision support (CDS) documents for pharmaceutical and clinical research — patient cohort analyses, treatment recommendation reports with GRADE evidence grading, biomarker integration, and statistical outputs (hazard ratios, survival curves). |
| tooluniverse-clinical-trial-design | Strategic clinical trial design feasibility assessment. Evaluates patient population sizing, biomarker prevalence, endpoint selection, comparator analysis, safety monitoring, and regulatory pathways. Use when planning Phase 1/2 trials or assessing trial feasibility. |
| tooluniverse-disease-research | Generate comprehensive disease research reports covering epidemiology, mechanisms, diagnostics, treatments, and ongoing trials. Use when asking about diseases, syndromes, or needing systematic disease analysis. |
| tooluniverse-literature-deep-research | Deep literature research with target disambiguation, evidence grading, and structured theme extraction. Resolves gene/protein IDs, identifies synonyms, synthesizes biological models, and generates testable hypotheses. Use for thorough literature reviews or target profiles. |
| tooluniverse-clinical-guidelines | Search and retrieve clinical practice guidelines from 12+ sources (NICE, WHO, ADA, AHA/ACC, NCCN, SIGN, CPIC, etc.). Covers cardiology, oncology, diabetes, pharmacogenomics, and more. Use when asking about treatment recommendations or standard of care. |
| tooluniverse-drug-research | Comprehensive drug research reports covering identity, pharmacology, targets, clinical trials, safety, pharmacogenomics, and ADMET. Use for drug profiling, safety assessment, or clinical development research. |
| tooluniverse-drug-repurposing | Identify drug repurposing candidates using target-based, compound-based, and disease-driven strategies. Finds new indications for approved drugs by analyzing targets, bioactivity, and safety profiles. |
| tooluniverse-drug-drug-interaction | Drug-drug interaction prediction and risk assessment. Analyzes CYP450/transporter mechanisms, severity classification, and provides management strategies. Supports polypharmacy analysis (3+ drugs) and alternative drug recommendations. |
| tooluniverse-rare-disease-diagnosis | Differential diagnosis for rare diseases based on phenotype and genetic data. Matches symptoms to HPO terms, identifies candidate diseases from Orphanet/OMIM, and interprets variants of uncertain significance. |
| tooluniverse-pharmacovigilance | Analyze drug safety signals from FDA adverse event reports, label warnings, and pharmacogenomic data. Calculates PRR/ROR, identifies serious adverse events, and assesses pharmacogenomic risk. |
| tooluniverse-clinical-trial-matching | Patient-to-trial matching for precision medicine and oncology. Ranks trials from ClinicalTrials.gov by molecular eligibility, clinical criteria, biomarker alignment, and geographic feasibility with a quantitative Trial Match Score (0-100). |
| literature-review | Systematic literature reviews across multiple databases (PubMed, arXiv, bioRxiv, Semantic Scholar). Produces professionally formatted reports with verified citations in APA, Nature, Vancouver styles. |
| tooluniverse-precision-oncology | Actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, clinical trials, and resistance mechanisms. |
| tooluniverse-cancer-variant-interpretation | Clinical interpretation of somatic mutations in cancer. Given gene+variant (e.g., EGFR L858R, BRAF V600E), assesses oncogenicity, therapeutic implications, and trial eligibility. |
| tooluniverse-variant-analysis | Production-ready VCF processing, variant annotation, and mutation analysis. Parses VCF files, annotates with ClinVar/gnomAD/COSMIC, and interprets clinical significance. |
| tooluniverse-variant-interpretation | Systematic clinical variant interpretation from raw calls to ACMG-classified recommendations. Aggregates evidence from ClinVar, gnomAD, literature, and population databases. |
| tooluniverse-structural-variant-analysis | Comprehensive structural variant (SV/CNV) analysis for clinical genomics. Classifies SVs, assesses pathogenicity, and interprets copy number alterations. |
| tooluniverse-polygenic-risk-score | Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles and interprets PRS percentiles. |
| tooluniverse-precision-medicine-stratification | Patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Identifies treatment-relevant subgroups and biomarker-driven therapy options. |
| tooluniverse-gwas-trait-to-gene | Discover genes associated with diseases and traits using GWAS Catalog (500k+ associations) and Open Targets Genetics locus-to-gene predictions. |
| tooluniverse-gwas-drug-discovery | Transform GWAS signals into drug targets and repurposing opportunities. Performs locus-to-gene mapping, druggability assessment, and existing drug identification. |
| tooluniverse-gwas-study-explorer | Compare GWAS studies and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics for cross-study meta-analysis. |
| tooluniverse-gwas-finemapping | Identify and prioritize causal variants at GWAS loci using statistical fine-mapping. Computes posterior probabilities and credible sets for causal variant identification. |
| tooluniverse-gwas-snp-interpretation | Interpret SNPs from GWAS studies by aggregating evidence from GWAS Catalog, Open Targets Genetics, and ClinVar. Retrieves variant-trait associations and functional annotations. |
| tooluniverse-phylogenetics | Phylogenetics and sequence analysis — alignment processing, evolutionary tree construction, and evolutionary metrics for pathogens or species. |
| tooluniverse-epigenomics | Epigenomics data processing — methylation array analysis (CpG filtering, differential methylation), chromatin accessibility, and histone modification analysis. |
| tooluniverse-rnaseq-deseq2 | RNA-seq differential expression analysis using PyDESeq2. Performs normalization, dispersion estimation, Wald testing, LFC shrinkage, and pathway enrichment. |
| tooluniverse-single-cell | Single-cell RNA-seq analysis using scanpy. Performs QC, normalization, PCA, UMAP, Leiden clustering, trajectory analysis, and cell type annotation. |
| tooluniverse-spatial-transcriptomics | Spatial transcriptomics data analysis — maps gene expression in tissue architecture. Supports 10x Visium, MERFISH, seqFISH, and Slide-seq platforms. |
| tooluniverse-spatial-omics-analysis | Computational analysis for spatial multi-omics data integration — spatially variable genes, domain annotation, and tissue-resolved omics. |
| tooluniverse-proteomics-analysis | Mass spectrometry proteomics analysis — protein quantification, differential expression, PTMs, and protein-protein interaction network construction. |
| tooluniverse-metabolomics | Metabolomics research — identifies metabolites and searches databases (HMDB 220k+ metabolites, MetaboLights, Metabolomics Workbench). |
| tooluniverse-metabolomics-analysis | Metabolomics data analysis — metabolite identification, quantification, pathway analysis, and metabolic flux from LC-MS, GC-MS, or NMR data. |
| tooluniverse-multi-omics-integration | Integrate transcriptomics, proteomics, epigenomics, genomics, and metabolomics for systems biology and precision medicine. |
| tooluniverse-multiomic-disease-characterization | Systems-level disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers. |
| tooluniverse-expression-data-retrieval | Retrieve gene expression and omics datasets from ArrayExpress and BioStudies with quality assessment and structured reports. |
| tooluniverse-gene-enrichment | Gene enrichment and pathway analysis using gseapy, PANTHER, STRING, Reactome. Supports GO enrichment, KEGG pathways, and 40+ ToolUniverse tools. |
| tooluniverse-systems-biology | Systems biology and pathway analysis using Reactome, KEGG, WikiPathways, Pathway Commons, and BioModels. Network modeling and pathway simulation. |
| tooluniverse-protein-interactions | Protein-protein interaction network analysis using STRING, BioGRID, and SASBDB. Maps interaction networks with confidence scores and functional modules. |
| tooluniverse-protein-structure-retrieval | Retrieve protein structure data from RCSB PDB, PDBe, and AlphaFold with quality assessment and comprehensive structural profiles. |
| tooluniverse-protein-therapeutic-design | Design novel protein therapeutics (binders, enzymes, scaffolds) using AI-guided de novo design — RFdiffusion, ProteinMPNN, and ESM. |
| tooluniverse-antibody-engineering | Antibody engineering and optimization for therapeutics — humanization, affinity maturation, developability assessment, and immunogenicity prediction. |
| tooluniverse-immune-repertoire-analysis | TCR/BCR repertoire analysis from sequencing data — clonality, diversity, V(D)J gene usage, clonal expansion, and antigen specificity prediction. |
| tooluniverse-immunotherapy-response-prediction | Predict patient response to immune checkpoint inhibitors using multi-biomarker integration — TMB, MSI, PD-L1, TIL signatures, and HLA typing. |
| tooluniverse-infectious-disease | Pathogen characterization and drug repurposing for infectious disease outbreaks. Identifies taxonomy, essential proteins, structural targets, and treatment options. |
| tooluniverse-crispr-screen-analysis | CRISPR screen analysis for functional genomics — pooled or arrayed screens (knockout/activation/interference) to identify essential genes and hits. |
| tooluniverse-target-research | Comprehensive biological target intelligence — protein info, structure, interactions, pathways, expression, variant landscape, and drug pipeline. |
| tooluniverse-network-pharmacology | Compound-target-disease network analysis for drug repurposing, polypharmacology discovery, and systems pharmacology. |
| tooluniverse-statistical-modeling | Statistical modeling on biomedical datasets — linear/logistic regression, mixed-effects models, survival analysis, and Bayesian methods. |
| tooluniverse-image-analysis | Biomedical microscopy image analysis — colony morphometry, cell counting, fluorescence quantification, and statistical comparison of imaging data. |
| literature-search | Comprehensive scientific literature search across PubMed, arXiv, bioRxiv, medRxiv using natural language queries powered by Valyu semantic search. |
| medrxiv-search | Search medRxiv medical preprints with natural language queries powered by Valyu semantic search. |
| clinical-trials-search | Search ClinicalTrials.gov with natural language queries — find trials by condition, enrollment status, and outcomes via Valyu. |
| drug-discovery-search | End-to-end drug discovery platform combining ChEMBL, DrugBank, targets, and FDA labels via natural language Valyu search. |
| drug-labels-search | Search FDA drug labels with natural language queries — indications, dosing, and safety data via Valyu. |
| chembl-search | Search ChEMBL bioactive molecules database — compounds, assay data, and bioactivity via Valyu semantic search. |
| open-targets-search | Search Open Targets drug-disease associations and target validation via Valyu semantic search. |
| patents-search | Search global patents with natural language queries — prior art, patent landscapes, and innovation tracking via Valyu. |
| drugbank-search | Search DrugBank comprehensive drug database — mechanisms, interactions, and safety data via Valyu semantic search. |
| arxiv-search | Search arXiv preprints (biology, medicine, AI) using natural language queries powered by Valyu semantic search. |
| gwas-database | Query NHGRI-EBI GWAS Catalog for SNP-trait associations by rs ID, disease/trait, or gene. Retrieve p-values and summary statistics for genetic epidemiology. |
| scikit-survival | Survival analysis and time-to-event modeling in Python — Kaplan-Meier, Cox regression, log-rank tests, and censored data handling using scikit-survival. |
Drug Safety & Chemical Biology
Click to expand skill list
| Skill | Description |
|---|---|
| tooluniverse-adverse-event-detection | Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100). |
| tooluniverse-binder-discovery | Discover novel small molecule binders for protein targets using structure-based and ligand-based approaches. Creates actionable reports with candidate compounds, ADMET profiles, and synthesis feasibility. |
| tooluniverse-chemical-compound-retrieval | Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Comprehensive compound profiles with identifiers, properties, bioactivity. |
| tooluniverse-chemical-safety | Comprehensive chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, FDA label safety data, DrugBank safety profiles, and STITCH chemical-protein interactions. |
| tooluniverse-drug-target-validation | Computational validation of drug targets across 10 dimensions: disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, and expression evidence. |
| tooluniverse-sequence-retrieval | Retrieve biological sequences (DNA, RNA, protein) from NCBI and ENA with gene disambiguation, accession type handling, and comprehensive sequence profiles. |
Medical Imaging & Pathology
Click to expand skill list
| Skill | Description |
|---|---|
| pydicom | Python library for working with DICOM medical imaging files. Reading, writing, modifying DICOM data, extracting pixel data, handling metadata and multi-frame files. |
| histolab | Digital pathology image processing toolkit for whole slide images (WSI). Process H&E or IHC stained tissue images, extract tiles from gigapixel slides. |
| pathml | Computational pathology toolkit for analyzing WSI and multiparametric imaging data. H&E stained images, multiplex immunofluorescence, spatial omics integration. |
| omero-integration | Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, for high-content screening workflows. |
| neurokit2 | Comprehensive biosignal processing: ECG, EEG, EDA, RSP, PPG, EMG, EOG signals. Cardiovascular signal analysis, neurophysiology, and physiological data processing. |
| neuropixels-analysis | Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, for neuroscience research. |
Healthcare ML & Clinical AI
Click to expand skill list
| Skill | Description |
|---|---|
| pyhealth | Comprehensive healthcare AI toolkit for developing ML models with clinical data (EHR, claims). Task definition API, model training, evaluation for clinical NLP and prediction. |
| scikit-learn | Machine learning in Python: supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning. |
| transformers | Pre-trained transformer models for NLP, computer vision, audio, and multimodal tasks. Text generation, classification, question answering, and biomedical NLP (BioBERT, ClinicalBERT). |
| shap | Model interpretability using SHAP (SHapley Additive exPlanations). Explain ML model predictions, compute feature importance, generate SHAP plots for biomedical models. |
| umap-learn | UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), for high-dimensional omics data. |
Health & Wellness Analytics
Click to expand skill list
| Skill | Description |
|---|---|
| nutrition-analyzer | Comprehensive nutrition analysis: macro/micronutrient tracking, dietary assessment, meal planning, food data lookup, and nutritional recommendations. |
| mental-health-analyzer | Mental health data analysis: mood tracking, symptom patterns, PHQ/GAD scoring, behavioral insights, and wellness recommendations. |
| sleep-analyzer | Sleep quality analysis: sleep stages, duration, efficiency metrics, circadian rhythm assessment, and sleep hygiene recommendations. |
| rehabilitation-analyzer | Rehabilitation progress tracking: functional assessments, exercise programs, recovery milestones, and outcome measurement for physical/occupational therapy. |
| fitness-analyzer | Fitness performance analysis: exercise tracking, strength/cardio metrics, training load, VO2max estimation, and periodization planning. |
| health-trend-analyzer | Longitudinal health trend analysis: vital sign tracking, biomarker trends, risk factor monitoring, and predictive health insights. |
| weightloss-analyzer | Weight management analytics: caloric balance, body composition tracking, progress monitoring, and evidence-based weight loss strategies. |
| goal-analyzer | Health goal tracking and analysis: SMART goal setting, progress metrics, habit formation, and motivational insights for wellness objectives. |
| occupational-health-analyzer | Occupational health assessment: workplace ergonomics, exposure risk, work-related illness surveillance, and return-to-work planning. |
| travel-health-analyzer | Travel medicine: destination health risks, vaccination requirements, malaria prophylaxis, altitude sickness, and traveler health preparation. |
| family-health-analyzer | Family health management: pediatric milestones, family medical history, preventive screening schedules, and multigenerational health tracking. |
| tcm-constitution-analyzer | Traditional Chinese Medicine constitution analysis: TCM body type assessment, pattern differentiation, herbal recommendations, and lifestyle guidance. |
| emergency-card | Generate emergency medical information cards with critical health data, medications, allergies, and emergency contacts for patient safety. |
| ai-analyzer | AI-powered comprehensive health data interpretation combining multiple biomarkers and health metrics for holistic wellness assessment. |
| wellally-tech | Technical framework for WellAlly health analytics platform: integration patterns, data pipelines, and health AI infrastructure. |
Mental Health & Crisis Intervention
Click to expand skill list
| Skill | Description |
|---|---|
| crisis-detection-intervention-ai | Detect crisis signals using NLP and mental health sentiment analysis. Implements suicide ideation detection, automated escalation, and crisis resource integration for mental health apps and recovery platforms. |
| crisis-response-protocol | Handle mental health crisis situations safely: crisis detection, safety protocols, emergency escalation, suicide prevention, and hotline integration for AI coaching applications. |
| hipaa-compliance | Ensure HIPAA compliance when handling PHI. Audit logging, data access controls, security event tracking, and compliance verification for health data applications. |
| clinical-diagnostic-reasoning | Identify and counteract cognitive biases in medical decision-making through systematic error analysis, differential diagnosis frameworks, and clinical judgment improvement. |
| speech-pathology-ai | AI-powered speech-language pathology: phoneme analysis, articulation visualization, voice disorder assessment, fluency intervention, AAC, and stuttering treatment support. |
| hrv-alexithymia-expert | Heart rate variability biometrics and emotional awareness training. HRV analysis, interoception training, biofeedback, vagal tone assessment, and autonomic nervous system evaluation. |
| adhd-daily-planner | ADHD-optimized daily planning: time-blind friendly scheduling, executive function support, dopamine-aware task design, and neurodivergent-friendly productivity systems. |
| grief-companion | Compassionate bereavement support, memorial creation, grief education, and healing journey guidance through the non-linear path of loss. |
| jungian-psychologist | Jungian analytical psychology: shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through depth psychology lens, and individuation process. |
| modern-drug-rehab-computer | Comprehensive addiction recovery knowledge system: evidence-based treatment (CBT, DBT, MI, EMDR, MAT), recovery resources, crisis intervention, and family systems for rehab environments. |
| recovery-community-moderator | Trauma-informed AI moderation for addiction recovery communities: harm reduction, 12-step traditions, conflict detection, and crisis post identification. |
Medical Device & Regulatory
Click to expand skill list
| Skill | Description |
|---|---|
| iso-13485-certification | Comprehensive toolkit for ISO 13485 QMS documentation for medical devices: gap analysis, Quality Manuals, procedures, Medical Device Files. Covers FDA QMSR, EU MDR compliance. |
🗂️ Scientific Databases
Expand/Collapse this category
Scientific Databases (Genomics & Variants)
Click to expand skill list
| Skill | Description |
|---|---|
| clinvar-database | Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine. |
| clinpgx-database | Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions. |
| cosmic-database | Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication. |
| ensembl-database | Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research. |
| gene-database | Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis. |
| geo-database | Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis. |
| ena-database | Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. |
| gget | CLI/Python toolkit for rapid bioinformatics queries with access to 20+ databases: Ensembl, UniProt, AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, BLAST, and more. |
| pysam | Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines. |
Scientific Databases (Proteins, Pathways & Drugs)
Click to expand skill list
| Skill | Description |
|---|---|
| alphafold-database | Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology. |
| pdb-database | Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery. |
| uniprot-database | Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For multi-database workflows, prefer bioservices (unified interface to 40+ services). |
| string-database | Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology. |
| kegg-database | Direct REST API access to KEGG (academic use). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. |
| reactome-database | Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology. |
| brenda-database | Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, substrate-specific enzyme info for biochemical research. |
| hmdb-database | Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for metabolomics. |
| metabolomics-workbench-database | Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, for metabolomics and biomarker discovery. |
| pubchem-database | Query PubChem via PUG-REST API (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for cheminformatics. |
| chembl-database | Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, for medicinal chemistry. |
| drugbank-database | Access comprehensive drug information from DrugBank including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. |
| zinc-database | Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening. |
| opentargets-database | Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification. |
| fda-database | Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis. |
| pubmed-database | Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. |
| openalex-database | Query and analyze scholarly literature using the OpenAlex database. Search for academic papers, analyze research trends, find works by authors or institutions. |
| biorxiv-database | Search bioRxiv preprint server by keywords, authors, date ranges, or categories, retrieving paper metadata for life sciences preprint discovery. |
| bioservices | Primary Python tool for 40+ bioinformatics services. Unified API for UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO — preferred for multi-database workflows. |
| uspto-database | Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, for IP analysis and prior art searches. |
Cancer Genomics Databases
Click to expand skill list
| Skill | Description |
|---|---|
| cbioportal-database | Query cBioPortal for cancer genomics: somatic mutations, copy number, gene expression, and survival data across hundreds of cancer studies. Cancer target validation, oncogene analysis, and patient-level genomic profiling. |
| depmap | Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity, and gene effect profiles. Identify cancer-specific vulnerabilities and synthetic lethal interactions. |
| imaging-data-commons | Query and download public cancer imaging data from NCI Imaging Data Commons. Access radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. |
Genomic & Molecular Databases
Click to expand skill list
| Skill | Description |
|---|---|
| bindingdb-database | Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Drug discovery, lead optimization, polypharmacology, and SAR studies. |
| gnomad-database | Query gnomAD for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance. Variant pathogenicity interpretation and rare disease genetics. |
| gtex-database | Query GTEx for tissue-specific gene expression, eQTLs, and sQTLs. Link GWAS variants to gene regulation and interpret non-coding variant effects. |
| interpro-database | Query InterPro for protein family, domain, and functional site annotations. Integrates Pfam, PANTHER, PRINTS, SMART, and 11+ databases for protein function prediction. |
| jaspar-database | Query JASPAR for transcription factor binding site profiles (PWMs/PFMs). Regulatory genomics, motif analysis, and GWAS regulatory variant interpretation. |
| monarch-database | Query the Monarch Initiative knowledge graph for disease-gene-phenotype associations. Integrates OMIM, ORPHANET, HPO, ClinVar for rare disease gene discovery. |
| tiledbvcf | Scalable VCF/BCF ingestion, storage, and parallel queries using TileDB for population genomics at scale. |
Structural Biology & Drug Discovery
Click to expand skill list
| Skill | Description |
|---|---|
| molecular-dynamics | Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Protein/small molecule systems, force fields, energy minimization, RMSD/RMSF analysis, free energy surfaces. |
| glycoengineering | Analyze and engineer protein glycosylation. Predict N/O-glycosylation sites, access glycoengineering tools (NetOGlyc, GlycoShield). Therapeutic antibody optimization and vaccine design. |
| adaptyv | Cloud laboratory platform for automated protein testing: binding assays, expression testing, thermostability, enzyme activity. Protein sequence optimization with NetSolP, SoluProt, ESM. |
| ginkgo-cloud-lab | Submit and manage protocols on Ginkgo Bioworks Cloud Lab for autonomous lab execution. Cell-free protein expression, protocol workflows, and biotech automation. |
🧬 Bioinformatics (gptomics bio-* suite)
Expand/Collapse this category
Bioinformatics Tools & Pipelines
Click to expand skill list
| Skill | Description |
|---|---|
| biopython | Primary Python toolkit for molecular biology: PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), BLAST workflows. |
| scikit-bio | Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, for microbiome analysis. |
| etetoolkit | Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics. |
| deeptools | NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization. |
| nextflow-development | Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use for RNA-seq, WGS/WES, or ATAC-seq from local FASTQs or public datasets (GEO/SRA). |
| fastq-analysis | SRA downloading, FASTQ quality control, STAR alignment, gene quantification, and single-cell kallisto/bustools pipelines for bulk and single-cell sequencing data. |
| geniml | Genomic interval data (BED files) for machine learning tasks. Train region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis. |
| gtars | High-performance genomic interval analysis in Rust with Python bindings. Genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models. |
| arboreto | Infer gene regulatory networks (GRNs) from gene expression data using GRNBoost2 and GENIE3 algorithms. For bulk RNA-seq and single-cell RNA-seq regulatory network inference. |
| lamindb | Open-source biological data framework for queryable, traceable, reproducible, and FAIR datasets (scRNA-seq, genomics, imaging). |
| dnanexus-integration | DNAnexus cloud genomics platform. Build apps/applets, manage data, dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development. |
| latchbio-integration | Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, Nextflow/Snakemake integration. |
Bioinformatics — Clinical Databases & Variant Analysis
Click to expand skill list
| Skill | Description |
|---|---|
| bio-clinical-databases-clinvar-lookup | Query ClinVar for clinical variant classifications, pathogenicity assertions, and review status. |
| bio-clinical-databases-dbsnp-queries | Query dbSNP for SNP frequency, allele, and functional annotation data. |
| bio-clinical-databases-gnomad-frequencies | Retrieve population allele frequencies from gnomAD for rare variant interpretation. |
| bio-clinical-databases-hla-typing | HLA typing from sequencing data using standard typing tools and databases. |
| bio-clinical-databases-myvariant-queries | Batch query MyVariant.info for aggregated variant annotations from multiple databases. |
| bio-clinical-databases-pharmacogenomics | PharmGKB/CPIC pharmacogenomics variant lookup for drug-gene interactions. |
| bio-clinical-databases-polygenic-risk | Calculate polygenic risk scores from GWAS summary statistics and genotype data. |
| bio-clinical-databases-somatic-signatures | Extract and classify mutational signatures from somatic variant catalogs (COSMIC). |
| bio-clinical-databases-tumor-mutational-burden | Compute tumor mutational burden (TMB) from somatic variant calls. |
| bio-clinical-databases-variant-prioritization | Rank and filter candidate variants by pathogenicity scores, inheritance, and phenotype match. |
| bio-variant-calling | GATK-based germline variant calling pipeline from aligned BAM/CRAM files. |
| bio-variant-calling-clinical-interpretation | Interpret variant calls in clinical context with ACMG guidelines. |
| bio-variant-calling-deepvariant | DeepVariant deep-learning variant caller for short-read WGS/WES data. |
| bio-variant-calling-filtering-best-practices | Apply VQSR and hard-filtering best practices to variant call sets. |
| bio-variant-calling-joint-calling | Joint genotyping across multiple samples for improved variant discovery. |
| bio-variant-calling-structural-variant-calling | Call structural variants (SVs) from long-read or paired-end sequencing. |
| bio-variant-annotation | Annotate VCF files with functional, population, and clinical consequence data. |
| bio-variant-normalization | Normalize variant representations (left-alignment, decomposition) for consistent comparison. |
| bio-vcf-basics | Read, write, and parse VCF files; filter by quality, region, and sample. |
| bio-vcf-manipulation | Advanced VCF manipulation: merging, splitting, reformatting, subset extraction. |
| bio-vcf-statistics | Compute variant statistics: ts/tv ratio, heterozygosity, depth distributions. |
| bio-gatk-variant-calling | End-to-end GATK HaplotypeCaller variant calling with BQSR and joint genotyping. |
| bio-copy-number-cnv-annotation | Annotate CNV calls with gene content, database overlap, and clinical significance. |
| bio-copy-number-cnv-visualization | Visualize copy number profiles and segment plots from WGS/WES data. |
| bio-copy-number-cnvkit-analysis | CNVKit copy number analysis for targeted sequencing and WES data. |
| bio-copy-number-gatk-cnv | GATK4 somatic copy number alteration calling pipeline. |
| bio-tumor-fraction-estimation | Estimate tumor purity and ploidy from allele frequencies and copy number data. |
| bio-ctdna-mutation-detection | Detect circulating tumor DNA mutations from liquid biopsy ultra-deep sequencing. |
| bio-cfdna-preprocessing | Process cell-free DNA sequencing data: adapter trimming, deduplication, QC. |
| bio-methylation-based-detection | Detect methylation-based cancer signals from cfDNA methylation data. |
| bio-longitudinal-monitoring | Track somatic variant evolution and clonal dynamics across serial samples. |
Bioinformatics — Sequencing & Read QC
Click to expand skill list
| Skill | Description |
|---|---|
| bio-fastq-quality | Assess FASTQ read quality with FastQC/MultiQC; generate per-sample QC reports. |
| bio-read-qc-adapter-trimming | Trim sequencing adapters with Trimmomatic, Cutadapt, or fastp. |
| bio-read-qc-contamination-screening | Screen reads for human/microbial contamination using FastQ Screen or Kraken. |
| bio-read-qc-fastp-workflow | End-to-end read QC and preprocessing with fastp including UMI handling. |
| bio-read-qc-quality-filtering | Apply quality-score and length filters to remove low-quality reads. |
| bio-read-qc-quality-reports | Aggregate multi-sample QC reports with MultiQC. |
| bio-read-qc-umi-processing | Deduplicate PCR duplicates using UMI-tools for accurate quantification. |
| bio-paired-end-fastq | Handle paired-end FASTQ files: validation, interleaving, splitting. |
| bio-alignment-io | Read/write SAM/BAM/CRAM alignment files with pysam and samtools. |
| bio-alignment-msa-parsing | Parse and analyze multiple sequence alignments (FASTA, ClustalW, Stockholm). |
| bio-alignment-msa-statistics | Compute MSA statistics: conservation, gap content, entropy. |
| bio-alignment-pairwise | Pairwise sequence alignment using Smith-Waterman, Needleman-Wunsch, BLAST. |
| bio-longread-alignment | Align long reads (ONT/PacBio) with minimap2; sort and index BAM files. |
| bio-longread-qc | Quality control for long-read sequencing: read length, N50, error rate. |
| bio-longread-medaka | Consensus polishing and variant calling with Oxford Nanopore Medaka. |
| bio-longread-structural-variants | Call large structural variants from long-read data with Sniffles/PBSV. |
| bio-basecalling | Base-call raw ONT signals with Dorado/Guppy; convert FAST5 to FASTQ. |
| bio-compressed-files | Handle compressed bioinformatics files: bgzip, tabix, zstd, htslib. |
| bio-format-conversion | Convert between bioinformatics formats: FASTQ↔FASTA, BAM↔CRAM, BED↔GTF. |
| bio-sequence-statistics | Compute sequence statistics: GC content, length distributions, complexity. |
| bio-read-sequences | Read and iterate over biological sequences from FASTA/FASTQ files. |
| bio-write-sequences | Write biological sequences to FASTA/FASTQ with metadata preservation. |
| bio-filter-sequences | Filter sequences by length, quality, pattern, or taxonomy label. |
| bio-batch-processing | Batch-process large bioinformatics datasets across samples and cohorts. |
| bio-rnaseq-qc | RNA-seq specific QC: strandedness, rRNA contamination, gene body coverage. |
| bio-long-read-sequencing-clair3-variants | Call variants from long-read sequencing with Clair3 deep-learning model. |
| bio-long-read-sequencing-isoseq-analysis | Iso-Seq full-length transcript analysis for isoform discovery. |
| bio-long-read-sequencing-nanopore-methylation | Call CpG methylation from Oxford Nanopore sequencing with Modbam2bed. |
| bio-splicing-qc | RNA splicing quality assessment: junction read coverage, novel splice sites. |
| bio-splicing-quantification | Quantify alternative splicing events: PSI/inclusion levels per isoform. |
| bio-sashimi-plots | Generate sashimi plots for visualizing RNA-seq splicing at specific loci. |
| bio-consensus-sequences | Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus; useful for sample-specific references and haplotype reconstruction. |
Bioinformatics — Differential Expression & Transcriptomics
Click to expand skill list
| Skill | Description |
|---|---|
| bio-de-deseq2-basics | DESeq2 differential expression analysis: design matrix, size factors, dispersion. |
| bio-de-edger-basics | EdgeR differential expression for count data with empirical Bayes dispersion. |
| bio-de-results | Extract, filter, and annotate DESeq2/EdgeR results tables. |
| bio-de-visualization | Volcano plots, MA plots, and heatmaps for differential expression results. |
| bio-differential-expression-batch-correction | Remove batch effects with ComBat/limma for multi-cohort DE analysis. |
| bio-differential-expression-timeseries-de | Time-series differential expression with splines and mixed models. |
| bio-differential-splicing | Detect differential alternative splicing events with rMATS or MAJIQ. |
| bio-isoform-switching | Identify isoform switching events with DRIMSeq and IsoformSwitchAnalyzeR. |
| bio-ribo-seq-orf-detection | Detect translated ORFs from ribosome profiling data with RiboTaper/Ribo-TISH. |
| bio-ribo-seq-riboseq-preprocessing | Preprocess ribosome profiling reads: adapter trimming, rRNA removal, alignment. |
| bio-ribo-seq-ribosome-periodicity | Assess triplet periodicity and ribosome footprint quality in Ribo-seq data. |
| bio-ribo-seq-ribosome-stalling | Identify ribosome stalling sites and pausing from Ribo-seq profiles. |
| bio-ribo-seq-translation-efficiency | Compute translation efficiency ratios from matched RNA-seq and Ribo-seq. |
Bioinformatics — Pathway & Network Analysis
Click to expand skill list
| Skill | Description |
|---|---|
| bio-pathway-go-enrichment | Gene Ontology enrichment analysis with clusterProfiler or g:Profiler. |
| bio-pathway-gsea | Gene Set Enrichment Analysis (GSEA) with pre-ranked or count-based statistics. |
| bio-pathway-kegg-pathways | KEGG pathway enrichment and visualization for metabolic/signaling pathways. |
| bio-pathway-reactome | Reactome pathway analysis with hierarchical enrichment and visualization. |
| bio-pathway-wikipathways | WikiPathways enrichment and network visualization. |
| bio-pathway-enrichment-visualization | Dot plots, enrichment maps, and network visualizations for pathway results. |
Bioinformatics — Single-Cell & Spatial Omics
Click to expand skill list
| Skill | Description |
|---|---|
| bio-single-cell-batch-integration | Integrate scRNA-seq datasets across batches with Harmony, BBKNN, scVI. |
| bio-single-cell-cell-annotation | Annotate single-cell clusters using marker genes and reference atlases. |
| bio-single-cell-cell-communication | Infer ligand-receptor cell-cell communication with CellChat or NicheNet. |
| bio-single-cell-clustering | Cluster single cells with Leiden/Louvain algorithms in Scanpy/Seurat. |
| bio-single-cell-data-io | Read/write AnnData, Seurat, and 10x Genomics h5ad/h5 formats. |
| bio-single-cell-doublet-detection | Remove doublets from scRNA-seq with Scrublet or DoubletFinder. |
| bio-single-cell-lineage-tracing | Reconstruct cell lineage trees from scRNA-seq with clonal barcodes. |
| bio-single-cell-markers-annotation | Identify cluster marker genes and auto-annotate cell types. |
| bio-single-cell-metabolite-communication | Infer metabolite-mediated intercellular communication from scRNA-seq. |
| bio-single-cell-multimodal-integration | Integrate scRNA-seq with ATAC, CITE-seq, or spatial using WNN/MultiVI. |
| bio-single-cell-perturb-seq | Analyze genetic perturbation screens from Perturb-seq / CROP-seq data. |
| bio-single-cell-preprocessing | Single-cell preprocessing: count filtering, normalization, HVG selection. |
| bio-single-cell-scatac-analysis | scATAC-seq peak calling, TF motif enrichment, and chromatin accessibility. |
| bio-single-cell-splicing | RNA velocity and splicing dynamics with scVelo or Alevin. |
| bio-single-cell-trajectory-inference | Infer pseudotime trajectories with Monocle3, PAGA, or Slingshot. |
| bio-spatial-transcriptomics-image-analysis | Analyze histology images co-registered with spatial transcriptomics data. |
| bio-spatial-transcriptomics-spatial-communication | Ligand-receptor communication analysis with spatial context (COMMOT, SpatialDE). |
| bio-spatial-transcriptomics-spatial-data-io | Load and process Visium, Slide-seq, MERFISH, and STARmap datasets. |
| bio-spatial-transcriptomics-spatial-deconvolution | Deconvolve cell type proportions in spatial spots with RCTD, SPOTlight. |
| bio-spatial-transcriptomics-spatial-domains | Identify spatially variable genes and tissue domains with SpatialDE/BANKSY. |
| bio-spatial-transcriptomics-spatial-multiomics | Integrate spatial transcriptomics with proteomics, metabolomics, or imaging. |
| bio-spatial-transcriptomics-spatial-neighbors | Build spatial neighbor graphs and perform neighborhood enrichment analysis. |
| bio-spatial-transcriptomics-spatial-preprocessing | Preprocess spatial transcriptomics: QC, normalization, spot filtering. |
| bio-spatial-transcriptomics-spatial-proteomics | Analyze spatial proteomics data from CODEX, IMC, or MIBI platforms. |
| bio-spatial-transcriptomics-spatial-statistics | Spatial statistics: Moran's I, spatial autocorrelation, co-localization. |
| bio-spatial-transcriptomics-spatial-visualization | Visualize spatial gene expression maps and tissue section overlays. |
Bioinformatics — Epigenomics & Chromatin
Click to expand skill list
| Skill | Description |
|---|---|
| bio-atac-seq-atac-peak-calling | Call ATAC-seq chromatin accessibility peaks with MACS2/MACS3. |
| bio-atac-seq-atac-qc | ATAC-seq quality control: TSS enrichment, fragment size, FRiP score. |
| bio-atac-seq-differential-accessibility | Differential chromatin accessibility between conditions with DESeq2/DiffBind. |
| bio-atac-seq-footprinting | Transcription factor footprinting from ATAC-seq with TOBIAS or HINT-ATAC. |
| bio-atac-seq-motif-deviation | TF motif deviation scoring with chromVAR for single-cell ATAC data. |
| bio-atac-seq-nucleosome-positioning | Infer nucleosome positioning from ATAC-seq fragment length distributions. |
| bio-chipseq-differential-binding | Differential ChIP-seq binding analysis with DiffBind. |
| bio-chipseq-motif-analysis | De novo and known motif discovery from ChIP-seq peaks with HOMER/MEME. |
| bio-chipseq-peak-annotation | Annotate ChIP-seq peaks with genomic features and nearest genes. |
| bio-chipseq-peak-calling | Call ChIP-seq peaks with MACS2 for TF binding and histone marks. |
| bio-chipseq-qc | ChIP-seq quality metrics: FRiP, SCC, phantompeakqualtools. |
| bio-chipseq-super-enhancers | Identify super enhancers from H3K27ac ChIP-seq with ROSE. |
| bio-chipseq-visualization | Heatmaps and aggregate profiles at peak regions with deepTools. |
| bio-hi-c-analysis-compartment-analysis | Call A/B compartments from Hi-C contact matrices. |
| bio-hi-c-analysis-contact-pairs | Process Hi-C contact pairs: filtering, deduplication, binning. |
| bio-hi-c-analysis-hic-data-io | Read and write Hi-C data formats: .hic, cool, mcool with cooler/hicstuff. |
| bio-hi-c-analysis-hic-differential | Differential Hi-C interaction analysis between conditions. |
| bio-hi-c-analysis-hic-visualization | Visualize Hi-C contact maps, TADs, and loops with pyGenomeTracks. |
| bio-hi-c-analysis-loop-calling | Detect chromatin loops from Hi-C data with Mustache or HICCUPS. |
| bio-hi-c-analysis-matrix-operations | Normalize Hi-C matrices: ICE, KR, VC; compute observed/expected. |
| bio-hi-c-analysis-tad-detection | Identify topologically associating domains (TADs) from Hi-C data. |
| bio-methylation-bismark-alignment | Align bisulfite sequencing reads and extract CpG methylation with Bismark. |
| bio-methylation-calling | Call CpG methylation from WGBS/RRBS alignments. |
| bio-methylation-dmr-detection | Identify differentially methylated regions (DMRs) with DSS or MethylKit. |
| bio-methylation-methylkit | Methylation analysis with MethylKit: CpG tiles, DMR calling, annotation. |
Bioinformatics — Metagenomics & Microbiome
Click to expand skill list
| Skill | Description |
|---|---|
| bio-metagenomics-abundance | Estimate microbial taxon abundances from shotgun metagenomics. |
| bio-metagenomics-amr-detection | Detect antimicrobial resistance genes with AMRFinder or RGI/CARD. |
| bio-metagenomics-functional-profiling | Functional profiling of metagenomes with HUMAnN3 for pathway/gene families. |
| bio-metagenomics-kraken | Taxonomic classification of metagenomic reads with Kraken2/Bracken. |
| bio-metagenomics-metaphlan | Clade-specific marker-based profiling of microbial communities with MetaPhlAn4. |
| bio-metagenomics-strain-tracking | Track microbial strains across samples with StrainPhlan or inStrain. |
| bio-metagenomics-visualization | Visualize microbiome composition with Krona charts and stacked bar plots. |
| bio-microbiome-amplicon-processing | Process 16S/ITS amplicon sequencing with QIIME2 or DADA2. |
| bio-microbiome-differential-abundance | Test differential microbial abundance with ANCOM-BC, MaAsLin2, or ALDEx2. |
| bio-microbiome-diversity-analysis | Alpha/beta diversity analysis: Shannon, PD, UniFrac, PCoA. |
| bio-microbiome-functional-prediction | Predict functional capacity from 16S data with PICRUSt2 or Tax4Fun. |
| bio-microbiome-qiime2-workflow | End-to-end QIIME2 workflow: denoising, diversity, differential abundance. |
| bio-microbiome-taxonomy-assignment | Assign taxonomy to ASVs/OTUs using SILVA, GTDB, or Greengenes2. |
Bioinformatics — Immunoinformatics & Flow Cytometry
Click to expand skill list
| Skill | Description |
|---|---|
| bio-immunoinformatics-epitope-prediction | Predict MHC-I/II epitopes from protein sequences with NetMHCpan/MHCflurry. |
| bio-immunoinformatics-immunogenicity-scoring | Score peptide immunogenicity for vaccine and neoantigen prioritization. |
| bio-immunoinformatics-mhc-binding-prediction | Predict peptide-MHC binding affinities for multiple alleles. |
| bio-immunoinformatics-neoantigen-prediction | Predict neoantigens from somatic mutations for personalized cancer vaccines. |
| bio-immunoinformatics-tcr-epitope-binding | Predict TCR-epitope binding with ERGO, pMTnet, or NetTCR. |
| bio-tcr-bcr-analysis-immcantation-analysis | Analyze B/T cell receptor repertoires with the Immcantation suite. |
| bio-tcr-bcr-analysis-mixcr-analysis | MiXCR V(D)J alignment and clonotype assembly for immune repertoires. |
| bio-tcr-bcr-analysis-repertoire-visualization | Visualize repertoire diversity, clonal expansion, and V-gene usage. |
| bio-tcr-bcr-analysis-scirpy-analysis | Single-cell TCR/BCR analysis integrated with scRNA-seq using Scirpy. |
| bio-tcr-bcr-analysis-vdjtools-analysis | Immune repertoire statistics and overlap analysis with VDJtools. |
| bio-flow-cytometry-bead-normalization | Normalize flow cytometry data using calibration beads. |
| bio-flow-cytometry-clustering-phenotyping | Cluster and phenotype cell populations with FlowSOM, PhenoGraph, or UMAP. |
| bio-flow-cytometry-compensation-transformation | Apply compensation matrices and biexponential/arcsinh transformations. |
| bio-flow-cytometry-cytometry-qc | Quality control for flow/mass cytometry: signal drift, spillover, outlier detection. |
| bio-flow-cytometry-differential-analysis | Statistical comparison of cell populations between conditions. |
| bio-flow-cytometry-doublet-detection | Detect and remove doublets from flow cytometry data. |
| bio-flow-cytometry-fcs-handling | Read, write, and manipulate FCS files with FlowCore/FlowKit. |
| bio-flow-cytometry-gating-analysis | Manual and algorithmic gating strategies for cell population identification. |
| bio-imaging-mass-cytometry-cell-segmentation | Segment cells in IMC images with Mesmer or CellProfiler. |
| bio-imaging-mass-cytometry-data-preprocessing | Preprocess imaging mass cytometry data: hot pixel removal, normalization. |
| bio-imaging-mass-cytometry-interactive-annotation | Interactively annotate cell types in IMC spatial datasets. |
| bio-imaging-mass-cytometry-phenotyping | Phenotype immune and tumor cells from multi-marker IMC panels. |
| bio-imaging-mass-cytometry-quality-metrics | Quality metrics for IMC acquisitions: signal-to-noise, tissue coverage. |
| bio-imaging-mass-cytometry-spatial-analysis | Spatial cell neighborhood analysis from imaging mass cytometry data. |
Bioinformatics — Multi-Omics Integration
Click to expand skill list
| Skill | Description |
|---|---|
| bio-multi-omics-data-harmonization | Harmonize multi-omics datasets: sample matching, batch correction, feature alignment. |
| bio-multi-omics-mixomics-analysis | Multi-omics factor analysis with mixOmics (DIABLO, MOFA, sPLS-DA). |
| bio-multi-omics-mofa-integration | Multi-Omics Factor Analysis (MOFA+) for latent factor discovery across modalities. |
| bio-multi-omics-similarity-network | Similarity Network Fusion (SNF) for patient stratification from multi-omics. |
Bioinformatics — Proteomics & Metabolomics
Click to expand skill list
| Skill | Description |
|---|---|
| bio-proteomics-data-import | Import DDA/DIA proteomics data from MaxQuant, Proteome Discoverer, FragPipe. |
| bio-proteomics-dia-analysis | DIA proteomics analysis with DIA-NN or Spectronaut. |
| bio-proteomics-differential-abundance | Differential protein abundance with limma, MSstats, or DEqMS. |
| bio-proteomics-peptide-identification | Peptide spectrum matching and database search result parsing. |
| bio-proteomics-protein-inference | Protein grouping, parsimony, and FDR control for proteomics experiments. |
| bio-proteomics-proteomics-qc | Proteomics QC: peptide counts, coverage, missing values, CV. |
| bio-proteomics-ptm-analysis | Post-translational modification analysis: phospho, ubiquitin, glycan enrichment. |
| bio-proteomics-quantification | Label-free, TMT/iTRAQ, and SILAC quantification workflows. |
| bio-proteomics-spectral-libraries | Build and use spectral libraries for DIA data analysis. |
| bio-metabolomics-lipidomics | Lipidomics data analysis: lipid class annotation, fatty acid composition. |
| bio-metabolomics-metabolite-annotation | Annotate mass spec features with HMDB, MZmine, SIRIUS, or MetFrag. |
| bio-metabolomics-msdial-preprocessing | MS-DIAL-based LC-MS/GC-MS data preprocessing and peak detection. |
| bio-metabolomics-normalization-qc | Metabolomics normalization: PQN, LOESS, median, batch correction. |
| bio-metabolomics-pathway-mapping | Map identified metabolites to KEGG, MetaCyc, or Reactome pathways. |
| bio-metabolomics-statistical-analysis | Univariate/multivariate stats for metabolomics: PCA, PLS-DA, ANOVA. |
| bio-metabolomics-targeted-analysis | Targeted metabolomics with MRM/SRM: calibration curves, quantification. |
| bio-metabolomics-xcms-preprocessing | XCMS-based LC-MS peak detection, alignment, and grouping. |
Bioinformatics — Structural Biology & Cheminformatics
Click to expand skill list
| Skill | Description |
|---|---|
| bio-structural-biology-alphafold-predictions | Use AlphaFold2/3 predictions: model quality assessment, confidence scores. |
| bio-structural-biology-modern-structure-prediction | Modern structure prediction with ESMFold, RoseTTAFold, and OpenFold. |
| bio-pdb-geometric-analysis | Geometric analysis of protein structures: distances, angles, contacts, RMSD. |
| bio-pdb-structure-io | Read and write PDB/mmCIF structure files with BioPython or Gemmi. |
| bio-pdb-structure-modification | Modify protein structures: add hydrogens, mutate residues, energy minimize. |
| bio-pdb-structure-navigation | Navigate and inspect PDB structures: chain, residue, atom selection. |
| bio-molecular-descriptors | Calculate molecular descriptors (RDKit): MW, LogP, TPSA, fingerprints. |
| bio-molecular-io | Read/write chemical structure formats: SDF, SMILES, MOL2, PDB with RDKit. |
| bio-reaction-enumeration | Enumerate reactions and products from SMARTS reaction templates. |
| bio-similarity-searching | Molecular similarity search: Tanimoto, fingerprint-based, scaffold hopping. |
| bio-substructure-search | Substructure searching in chemical databases using SMARTS patterns. |
| bio-virtual-screening | Virtual screening workflows: docking, scoring, pose filtering with AutoDock/Vina. |
| bio-admet-prediction | Predict ADMET properties: absorption, distribution, metabolism, excretion, toxicity. |
Bioinformatics — Epidemiological & Causal Genomics
Click to expand skill list
| Skill | Description |
|---|---|
| bio-epidemiological-genomics-amr-surveillance | Antimicrobial resistance surveillance from genomic epidemiology data. |
| bio-epidemiological-genomics-pathogen-typing | Pathogen molecular typing: MLST, wgMLST, cgMLST for outbreak analysis. |
| bio-epidemiological-genomics-phylodynamics | Phylodynamics: molecular clock, population dynamics, BEAST2/TreeTime. |
| bio-epidemiological-genomics-transmission-inference | Infer transmission networks from pathogen genomics with TransPhylo/outbreaker2. |
| bio-epidemiological-genomics-variant-surveillance | Track pathogen variant emergence and spread from genomic surveillance. |
| bio-causal-genomics-colocalization-analysis | Colocalization analysis of GWAS and eQTL signals with coloc or eCAVIAR. |
| bio-causal-genomics-fine-mapping | Fine-map causal variants at GWAS loci with SuSiE or FINEMAP. |
| bio-causal-genomics-mediation-analysis | Causal mediation analysis for gene expression intermediaries. |
| bio-causal-genomics-mendelian-randomization | Two-sample Mendelian randomization with MR-Base/TwoSampleMR. |
| bio-causal-genomics-pleiotropy-detection | Detect horizontal pleiotropy and heterogeneity in MR analyses. |
| bio-genome-engineering-base-editing-design | Design base editors (CBE/ABE) for precise single-base correction. |
| bio-genome-engineering-grna-design | Design and score CRISPR guide RNAs with Cas-OFFinder and CRISPOR. |
| bio-genome-engineering-hdr-template-design | Design HDR templates for precise knock-in via homology-directed repair. |
| bio-genome-engineering-off-target-prediction | Predict CRISPR off-target sites genome-wide for safety assessment. |
| bio-genome-engineering-prime-editing-design | Design pegRNAs and nickase gRNAs for prime editing experiments. |
| bio-crispr-screens-base-editing-analysis | Analyze base editing screens: guide efficiency, editing outcomes. |
| bio-crispr-screens-batch-correction | Correct batch effects in CRISPR screen data across replicates. |
| bio-crispr-screens-crispresso-editing | Quantify editing outcomes with CRISPResso2 from amplicon sequencing. |
| bio-crispr-screens-hit-calling | Call hits from CRISPR screens using MAGeCK, BAGEL2, or casTLE. |
| bio-crispr-screens-jacks-analysis | CRISPR screen analysis with JACKS hierarchical Bayesian model. |
| bio-crispr-screens-library-design | Design CRISPR screen libraries: guide selection, controls, coverage. |
| bio-crispr-screens-mageck-analysis | MAGeCK MLE/RRA analysis for CRISPR pooled screens. |
| bio-crispr-screens-screen-qc | Quality control for CRISPR screens: Gini index, read distribution. |
🔬 Omics & Computational Biology
Expand/Collapse this category
Single-Cell & Spatial Omics
Click to expand skill list
| Skill | Description |
|---|---|
| anndata | Working with annotated data matrices in Python for single-cell genomics analysis, managing experimental measurements with metadata and large-scale omics data. |
| scanpy | Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory. |
| scvi-tools | Deep learning for single-cell analysis: data integration/batch correction (scVI/scANVI), ATAC-seq (PeakVI), CITE-seq (totalVI), multiome (MultiVI), spatial deconvolution (DestVI). |
| single-cell-rna-qc | Quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. |
| cellxgene-census | Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis. |
| pydeseq2 | Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots. |
| bulk-combat-correction | Remove batch effects from merged bulk RNA-seq or microarray cohorts using pyComBat, with corrected matrix export and pre/post correction visualizations. |
| bulk-deg-analysis | Bulk RNA-seq DEG pipeline: gene ID mapping, DESeq2 normalization, statistical testing, visualization, and pathway enrichment via OmicVerse. |
| bulk-deseq2-analysis | PyDESeq2-based differential expression analysis with ID mapping, DE testing, fold-change thresholding, and enrichment visualization. |
| bulk-stringdb-ppi | Query STRING for protein interactions, build PPI graphs with pyPPI, and render network figures for bulk gene lists. |
| bulk-to-single-deconvolution | Convert bulk RNA-seq cohorts to synthetic single-cell datasets using Bulk2Single workflow for cell fraction estimation and beta-VAE generation. |
| bulk-trajblend-interpolation | Extend scRNA-seq developmental trajectories with BulkTrajBlend by generating intermediate cells from bulk RNA-seq using beta-VAE and GNN models. |
| bulk-wgcna-analysis | Run PyWGCNA through OmicVerse — co-expression module construction, eigengene visualization, and hub gene extraction. |
| single-annotation | Single-cell annotation workflows: SCSA, MetaTiME, CellVote, CellMatch, GPTAnno, and weighted KNN transfer for annotating cell types across modalities. |
| single-cellphone-db | Run CellPhoneDB v5 on annotated single-cell data to infer ligand-receptor networks and produce CellChat-style visualizations. |
| single-clustering | Single-cell clustering workflow: QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration in OmicVerse. |
| single-downstream-analysis | OmicVerse downstream tutorials covering AUCell scoring, metacell DEG, and related exports for single-cell data. |
| single-multiomics | OmicVerse multi-omics tutorials: MOFA, GLUE pairing, SIMBA integration, TOSICA transfer, and StaVIA cartography. |
| single-preprocessing | Single-cell preprocessing in OmicVerse: QC, normalization, HVG detection, PCA/embedding pipelines (CPU/GPU). |
| single-to-spatial-mapping | Map scRNA-seq atlases onto spatial transcriptomics slides using Single2Spatial workflow for deep-forest training and marker visualization. |
| single-trajectory | OmicVerse trajectory workflows: PAGA, Palantir, VIA, velocity coupling, and fate scoring. |
| spatial-tutorials | Spatial transcriptomics tutorials: preprocessing, deconvolution, and downstream modeling across Visium, Visium HD, Stereo-seq, and Slide-seq. |
| tcga-preprocessing | Ingest TCGA sample sheets, expression archives, and clinical carts into OmicVerse, with survival metadata initialization and AnnData export. |
| gsea-enrichment | Gene set enrichment analysis in OmicVerse with correct geneset format handling for loading pathway databases and running GSEA. |
Cheminformatics & Drug Discovery
Click to expand skill list
| Skill | Description |
|---|---|
| rdkit | Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity. |
| datamol | Pythonic RDKit wrapper with simplified interface for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformer generation. |
| medchem | Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. |
| diffdock | Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. |
| molfeat | Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML. |
| deepchem | Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, for drug discovery ML. |
| torchdrug | Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs. |
| torch_geometric | Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, molecular property prediction, for geometric |