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Related Experiment Video

Updated: Apr 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

An agentic AI system for automated pharmacogenomic recommendation generation.

Mike Zack¹, Anton Savinkov², Danil Stupichev²

¹PGxAI Inc., Palo Alto, CA, USA. mz@pgx.ai.

NPJ Digital Medicine

|April 15, 2026

Summary

An AI system automates pharmacogenomic guideline creation, improving drug therapy recommendations. This agentic AI offers scalable, evidence-based decision support for personalized medicine.

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Area of Science:

Biomedical Informatics
Pharmacogenomics
Artificial Intelligence

Background:

Current pharmacogenomic guideline curation is manual, slow, and limited in scope.
Tailoring drug therapy requires genetic profile analysis, which is challenging with existing methods.

Purpose of the Study:

To develop an automated, scalable agentic AI system for generating pharmacogenomic recommendations.
To improve the efficiency and coverage of clinical pharmacogenomic guideline creation.

Main Methods:

Utilized large language models (LLMs) guided by structured evidence.
Developed a modular pipeline to process biomedical literature and FDA drug labels.
Extracted and aggregated clinically relevant entities to generate gene-drug recommendations.

Main Results:

Achieved high accuracy (91.9%) in extracting clinically relevant entities.
Generated phenotype-specific dosing recommendations for gene-drug pairs.
Expert evaluations showed the AI system outperformed leading LLM baselines in clarity and concordance.

Conclusions:

Agentic AI can automate end-to-end pharmacogenomic evidence synthesis.
This approach enables broader population coverage and faster guideline updates.
The system provides consistent and explainable decision support for personalized medicine.