NexusMed.ai

Advancing Medication Safety and Drug Discovery with Agentic AI

Pharmacovigilance × Agentic AI

Predict the Harm.
Prevent the Outcome.

Grounding every adverse drug reaction prediction in real-world evidence — retrieval-augmented, agentic, and powered by two decades of large-scale safety data.

Explore Our Research Collaborate With Us

Our Research

NexusMed.ai works to understand, predict, and prevent Adverse Drug Reactions (ADRs), bringing together large-scale pharmacovigilance data, advanced information retrieval, and clinical expertise. Our central focus is structured and unstructured ADR outcome predictions — forecasting the severity and clinical trajectory of adverse events at System Organ Class (SOC) level — where we confront the challenges of unstructured case narratives and the limitations of today's benchmarks.

We leverage retrieval as a first-class component of prediction. Rather than relying on model parameters alone, we ground every prediction in evidence drawn from real-world safety reports and unified medical language system. Our mission is to strengthen patient safety and support informed prescribing by surfacing the hidden patterns within drug safety data.

Current Projects

Pharmacovigilance Database Mining Mining large-scale safety databases (FAERS, MedDRA, UMLS, PubChem) to detect and predict ADRs across both structured records and unstructured narratives.
Multi-Modal Retrieval Infrastructure Developing scalable BM25, dense vector, and GraphRAG databases that integrate lexical, semantic, and relational evidence for explainable pharmacovigilance analytics
Hybrid Retrieval Architecture Designing retrieval-augmented pipelines that combine lexical, semantic, and graph-based search to surface the most relevant ADR evidence for downstream AI models.
MCP-Powered Deep Research Agent Building an agentic system on fine-tuned LLMs that re-ranks retrieved evidence and refines queries during inference and content generation.
Natural Language Knowledge Exploration Supporting both natural-language and expert-mode queries to interactively explore over two decades of FAERS data at scale.
Real-Time Clinical Intelligence Developed a patient-centric, production-ready interface with a portable deployment framework, enabling real-time ADR reporting and interactive visualizations to enhance clinical decision-making.

Platform

Both platforms are available upon request — request a demo.

ADR Hybrid Retrieval A retrieval-augmented knowledge graph for adverse drug reaction prediction. Ask questions in natural language and get answers grounded in two decades of FAERS reports, combining lexical, semantic, and graph-based evidence.

Open GraphRAG App AMIA 2026 Slides

Drug Discovery Predict protein–ligand complexes with AlphaFold 3, Chai-1, and Boltz-2 on a private GPU farm. Screen candidate compounds by predicted binding affinity, and validate top hits with AutoDock Vina, GNINA, and OpenMM molecular dynamics.

Open Structure Prediction

Our Team

Dr. David Guo Principal Developer, Dual PhDs in Information Technology (AI/ML) and Biochemistry AI/ML in Healthcare and FinTech
Dr. Ya Anna Dai Assistant Professor, Finance Corporate Finance, Financial Valuation
Dr. Kim-Kwang Raymond Choo Professor, Information Systems and Cyber Security; Cloud Technology Endowed Professorship III Blockchain, AI in Cybersecurity

Collaborations

We are seeking partners across academic and industry organizations, clinical communities, and regulatory-science teams to advance research in drug safety and retrieval-augmented AI. We welcome new collaborations in pharmacovigilance data sharing, clinical validation, and methodological research. If you are interested in contributing to these efforts or exploring potential collaboration opportunities, we would be pleased to connect and discuss how we can work together, please get in touch.

Selected Publications

Contact Us

Email: contact@nexusmed.ai

Donations

NexusMed.ai is a research organization supported by research funding and charitable donations. These contributions primarily support the computing infrastructure, research activities, and open-source development behind our projects. We welcome support in many forms, including financial contributions, computing hardware (such as GPUs) donations, and cloud computing credits. Ongoing contributions from a broad community of supporters are essential to advancing our research, sustaining our open-source initiatives, and maintaining our public charity mission. To learn more about sponsorship opportunities and benefits, please contact Dr. David Guo.