From Literature to Hypotheses: An AI Co-Scientist System for Biomarker-Guided Drug Combination Hypothesis Generation

2026-02-28

AI Summary

This work addresses the challenge of efficiently translating complex biomarker mechanisms—burdened by the exponential growth of biomedical literature and databases—into testable drug combination hypotheses. To this end, we propose CoDHy, a human–AI collaborative research system that integrates structured databases and unstructured literature to construct a task-oriented knowledge graph. CoDHy introduces a novel framework combining knowledge graph embeddings with agent-based reasoning to enable traceable, intervenable, and transparent generation, validation, and ranking of drug combination hypotheses. Through an interactive interface and an end-to-end workflow, CoDHy effectively supports researcher-driven exploratory hypothesis generation and decision-making in translational oncology, with its feasibility and practical utility demonstrated in real-world scenarios.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

The rapid growth of biomedical literature and curated databases has made it increasingly difficult for researchers to systematically connect biomarker mechanisms to actionable drug combination hypotheses. We present AI Co-Scientist (CoDHy), an interactive, human-in-the-loop system for biomarker-guided drug combination hypothesis generation in cancer research. CoDHy integrates structured biomedical databases and unstructured literature evidence into a task-specific knowledge graph, which serves as the basis for graph-based reasoning and hypothesis construction. The system combines knowledge graph embeddings with agent-based reasoning to generate, validate, and rank candidate drug combinations, while explicitly grounding each hypothesis in retrievable evidence. Through a web-based interface, users can configure the scientific context, inspect intermediate results, and iteratively refine hypotheses, enabling transparent and researcher-steerable exploration rather than automated decision-making. We demonstrate CoDHy as a system for exploratory hypothesis generation and decision support in translational oncology, highlighting its design, interaction workflow, and practical use cases.

Problem

• biomarker
• drug combination
• hypothesis generation
• biomedical literature
• knowledge graph

Innovation

• knowledge graph
• agent-based reasoning
• biomarker-guided drug combination
• human-in-the-loop AI
• hypothesis generation