数据与复现药物发现与化学信息学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

pharmacogenomics-agent

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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

OpenClawNanoClaw分析处理复现实验pharmacogenomics-agent🧠 bioos extended suitedrug discovery & designpharmacogenomics

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

技能摘要

2 min

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

原始文档

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) ，用于 drug selection。
To predict drug response ，使用 transcriptomic 、 proteomic biomarkers。
用于 calculating polygenic risk scores ，用于 drug efficacy/toxicity。
When optimi。