Empowering biomedical discovery with AI agents
Authors: Shanghua Gao (Harvard University), Ada Fang (Harvard University), Yepeng Huang (Harvard University), Valentina Giunchiglia (Harvard University, Imperial College London), Ayush Noori (Harvard University), Jonathan Richard Schwarz (Harvard University), Yasha Ektefaie (Harvard University), Jovana Kondic (Massachusetts Institute of Technology), Marinka Žitnik (Broad Institute, Harvard University)
Abstract
We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
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Abstract
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The AI Scientist presents a framework for fully autonomous research, whereas this paper envisions agents collaborating with human biomedical researchers.
Abstract
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Abstract
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Transforming Science with Large Language Models surveys AI support across research tasks, including experimentation and content generation.
Abstract
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Abstract
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging as running real-world experiments is often prohibitively expensive or infeasible. In this work we introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. DISCOVERYWORLD contains a variety of different challenges, covering topics as diverse as radioisotope dating, rocket science, and proteomics, to encourage development of general discovery skills rather than task-specific solutions. DISCOV…
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DrugAgent uses specialized AI agents, machine learning, and biomedical knowledge sources to identify drug-repurposing opportunities.
Abstract
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Abstract
AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from text prompts to analyze data or fit statistical models. One area that these tools can make a substantial impact is in research discovery and summarization. Standalone tools and plugins to chatbots are being developed that allow researchers to more quickly find relevant literature than pre-2023 sear…
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Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI AgentsJiacheng Miao, Joe R. Davis, Yaohui Zhang et al. · 2025 · Artificial Intelligence · preprint, not peer reviewed · cited 34 times · AI summary available
Paper2Agent uses multiple agents to turn research papers and their code into interactive assistants, applying AI agents to research reuse.
Abstract
We introduce Paper2Agent, an automated framework that converts research papers into AI agents. Paper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery. Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant. It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (…
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