Empowering biomedical discovery with AI agents

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   Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

   Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine et al. · 2025 · Computation and Language · preprint, non relu par les pairs · cité 128 fois · Résumé IA disponible

   Résumé scientifique

   The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we …

2. Très proche Pas pertinent

   The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

   Chris Lu, Cong Lu, Robert Tjarko Lange et al. · 2024 · Artificial Intelligence · preprint, non relu par les pairs · cité 1 355 fois · Résumé IA disponible

   Résumé scientifique

   One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aids to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its …

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   Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

   Bang Liu, Xinfeng Li, Jiayi Zhang et al. · 2025 · Artificial Intelligence · preprint, non relu par les pairs · cité 178 fois · Résumé IA disponible

   Résumé scientifique

   The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges. This survey provides a comprehensive overview, framing intelligent agents within a modular, brain-inspired architecture that integrates principles from cognitive science, neuroscience, and computational research. We structure our exploration into four interconnected parts.…

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   Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation

   Steffen Eger, Yong Cao, Jennifer D'Souza et al. · 2025 · Computation and Language · cité 65 fois · Résumé IA disponible

   Résumé scientifique

   With the advent of large multimodal language models, science is now at a threshold of an AI-based technological transformation. Recently, a plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. This includes all aspects of the research cycle, especially (1) searching for relevant literature; (2) generating research ideas and conducting experimentation; generating (3) text-based and (4) multimodal content (e.g., scientific figures and diagrams); and (5) AI-based automatic peer review. In this survey, we provide an in-depth overview over these exciting recent developments, whic…

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   DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents

   Peter Jansen, Marc-Alexandre Côté, Tushar Khot et al. · 2024 · Artificial Intelligence · cité 128 fois · Résumé IA disponible

   Résumé scientifique

   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: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning

   Yoshitaka Inoue, Tianci Song, Tianfan Fu · 2024 · Artificial Intelligence · Résumé IA disponible

   Résumé scientifique

   Drug repurposing offers a promising avenue for accelerating drug development by identifying new therapeutic potentials of existing drugs. In this paper, we propose a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Our framework comprises several specialized agents: an AI Agent trains robust drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the drug-gene interaction database (DGIdb), DrugBank, Comparative Toxicogenomics Database (CTD), and Search Tool for Interactions of Chemicals (STITCH) to systematically extract DTIs; and a Search Agent interacts with biomedical literature to an…

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   AI and Generative AI for Research Discovery and Summarization

   Mark Glickman, Yi Zhang · 2024 · Computation and Language · cité 53 fois · Pas encore résumé

   Résumé scientifique

   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 Agents

   Jiacheng Miao, Joe R. Davis, Yaohui Zhang et al. · 2025 · Artificial Intelligence · preprint, non relu par les pairs · cité 34 fois · Résumé IA disponible

   Résumé scientifique

   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 (…