^{Example of a knowledge graph}

Unified Biomedical Knowledge Graph

Q-Scientist represents genes, proteins, pathways, diseases, compounds, phenotypes, and experimental observations as interconnected biological entities rather than isolated datasets. By integrating public biomedical resources with proprietary research data and scientific literature, the Knowledge Graph provides a unified foundation for scientific reasoning. This enables AI agents to analyze multiple sources of evidence simultaneously, producing more comprehensive and biologically meaningful insights.

Native Single-Cell and Multi-Omics Data Integration

This release significantly expands the platform's data ingestion capabilities. Q-Scientist now supports native ingestion of single-cell RNA sequencing (scRNA  — seq), spatial transcriptomics, proteomics, metabolomics, CRISPR perturbation studies, and other multi-omics datasets. During ingestion, biological entities are automatically harmonized with existing graph concepts while preserving cellular context, lineage relationships, experimental metadata, and data provenance. The resulting graph creates a consistent representation of biological knowledge across diverse experimental modalities.

Graph AI for Target Discovery and Drug Repurposing

Once incorporated into the Knowledge Graph, graph machine learning algorithms identify biological relationships that are not explicitly represented in existing databases. These capabilities support gene prioritization, disease mechanism discovery, biomarker identification, therapeutic target discovery, and drug repurposing by combining network topology with experimental evidence and biological context. Every prediction is accompanied by confidence estimates, supporting evidence, and explainable biological paths, enabling researchers to understand the reasoning behind each recommendation.

Active Learning from Experimental Evidence

This release also introduces an active learning framework that continuously improves predictive performance as new experimental data becomes available. Wet-lab validation results, screening campaigns, customer-specific datasets, and other experimental observations are automatically incorporated into the Knowledge Graph to refine graph representations and update downstream predictive models. AI agents identify regions of high uncertainty, recommend the most informative follow-up experiments, and continuously refine biological hypotheses based on newly generated evidence.

Continuous Scientific Intelligence

Together, these capabilities establish a continuously evolving scientific intelligence platform where knowledge graphs, graph machine learning, and agentic AI operate as a closed-loop discovery system. As additional experimental evidence is generated, the platform continuously expands its knowledge base, improves prediction quality, and accelerates target identification, disease understanding, biomarker discovery, and drug repurposing across pharmaceutical R&D.

Request a Demo

Interested in seeing the Knowledge Graph in action? Fill out the form below or reach out to us at contact@quantori.com to schedule a personalized demonstration and learn how Q-Scientist supports target discovery, drug repurposing, and AI-driven scientific reasoning.