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pharmacogenomics-agent

Maintainer FreedomIntelligence · Last updated April 1, 2026

Pharmacogenomics analysis: variant-drug interaction prediction and dosing recommendations.

OpenClawNanoClawAnalysisReproductionpharmacogenomics-agent🧠 bioos extended suitedrug discovery & designpharmacogenomics

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/pharmacogenomics-agent

Skill Snapshot

2 min

The Pharmacogenomics Agent integrates AI and multi-omics data to predict individual drug responses, optimize medication dosing, and minimize adverse events. It implements CPIC guidelines while leveraging deep learning for complex polygenic drug response phenotypes.
When interpreting pharmacogenomic variants (CYP450, HLA, transporters) for drug selection.
To predict drug response using transcriptomic and proteomic biomarkers.
For calculating polygenic risk scores for drug efficacy/toxicity.
When optimizing doses for narrow therapeutic index drugs.

Source Doc

Variant Interpretation: Translates star allele genotypes (*1/*2) into metabolizer phenotypes and actionable CPIC recommendations.
Multi-Omics Response Prediction: Deep learning models (DeepDRA, MOViDA) integrate genomic, transcriptomic, and proteomic features for drug response prediction.
Polygenic Risk Scoring: Combines effects of thousands of variants to stratify patients beyond single-gene pharmacogenomics.
Adverse Event Prediction: Identifies genetic risk factors for serious adverse reactions (HLA associations, G6PD deficiency).
Dose Optimization: AI-guided dosing for warfarin, tacrolimus, fluoropyrimidines, thiopurines, and other PGx-guided drugs.
Drug-Drug-Gene Interactions: Detects complex interactions where genetic variants modify drug interaction severity.

Gene	Drugs	Clinical Impact
CYP2D6	Codeine, tamoxifen, antidepressants	Metabolizer status affects efficacy/toxicity
CYP2C19	Clopidogrel, PPIs, antidepressants	Loss-of-function affects activation
CYP2C9/VKORC1	Warfarin	Dose requirements vary 10-fold
TPMT/NUDT15	Thiopurines	Myelosuppression risk
DPYD	Fluoropyrimidines	Severe/fatal toxicity in deficient patients
HLA-B*57:01	Abacavir	Hypersensitivity screening
HLA-B*15:02	Carbamazepine	SJS/TEN risk in Asian populations

Input: Patient genotype data (VCF, genotyping array), medication list, clinical parameters.
Star Allele Calling: Translate variants to star alleles using Stargazer or PharmCAT.
Phenotype Assignment: Determine metabolizer status (PM, IM, NM, UM) for each gene.
Guideline Lookup: Retrieve CPIC/DPWG recommendations for patient's medications.
Multi-Omics Prediction: Apply deep learning for complex response phenotypes.
Output: Drug-specific recommendations, dose adjustments, alternative medications, interaction alerts.

When interpreting pharmacogenomic variants (CYP450, HLA, transporters) for drug selection.
To predict drug response using transcriptomic and proteomic biomarkers.
For calculating polygenic risk scores for drug efficacy/toxicity.
When optimi.