AI Agent

A comprehensive review from Cell envisioning 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents integrating AI models and biomedical tools with expert human oversight. Covers the landscape of AI agents for biomedical discovery, including virtual laboratories, autonomous experimentation, and human-AI collaboration frameworks.

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 expert human oversight. This review covers the landscape of AI agents for biomedical discovery, including virtual laboratories, autonomous experimentation, and human-AI collaboration frameworks.

A comprehensive review from Nature Reviews Cancer examining how AI agents (beyond traditional ML classifiers) are transforming cancer research and oncology. Covers LLM-powered agents for clinical decision support, drug discovery, treatment planning, and patient care, including agentic systems capable of logical reasoning, multi-step planning, and tool use in oncology contexts.

Since 2022, artificial intelligence (AI) methods have progressed far beyond their established capabilities of data classification and prediction. Large language models (LLMs) can perform logical reasoning, multi-step planning, and tool use, enabling a new paradigm of AI agents for cancer research and oncology. This review examines how AI agents are transforming clinical decision support, drug discovery, treatment planning, and patient care in oncology.

AI-MARRVEL (AIM) is a knowledge-driven AI system for diagnosing Mendelian disorders that uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. It incorporates expert-engineered features to recapitulate the complex decision-making processes in molecular diagnosis. AIM doubled the rate of accurate genetic diagnosis across three independent real-world cohorts compared to benchmarked methods. Its confidence metric achieved 98% precision and identified 57% of diagnosable cases from 871 unsolved cases. AIM also demonstrated potential for novel disease gene discovery.

Multi-agent LLM system that learns to chain bioinformatics tools into workflows via exploration and memory retrieval; natural-language tasks. Paper introduces BioMed-AQA (327 tasks, ~77% success) and reports cross-omics, ML modelling, and pathology segmentation examples.

A general-purpose biomedical AI agent designed to autonomously execute diverse biomedical tasks across 25 domains. It integrates large language models (LLMs), retrieval-augmented planning, and code-based execution to perform workflows like causal gene prioritization, drug repurposing, and rare disease diagnosis without task-specific tuning.

CRISPR-GPT is an LLM-based agent system for automating gene-editing experiments. It leverages large language models to guide researchers through the entire CRISPR experiment workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation, making gene-editing more accessible to non-expert researchers.

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models have not been fully leveraged for automated experimental design in molecular biology. Here we present CRISPR-GPT, an LLM-based agent system for automating gene-editing experiments across the entire CRISPR workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation.

ChemCrow is an LLM-based agent system that augments large language models with 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. It integrates tools for organic synthesis, drug discovery, and materials design, allowing the agent to plan syntheses, analyze chemical properties, and execute complex chemical tasks through natural language interaction.

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. Here we introduce ChemCrow, an LLM-based agent that integrates 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. ChemCrow successfully plans syntheses, analyzes chemical properties, and executes complex chemical tasks through natural language interaction.

Co-Scientist is a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. It aims to help scientists discover new original knowledge by formulating demonstrably novel research hypotheses for experimental validation, conditioned on research objectives and prior scientific evidence.

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental validation.

DeepRare is a multi-agent system for rare disease differential diagnosis that integrates large language models with structured medical knowledge to provide traceable reasoning. It aims to reduce the diagnostic odyssey for rare disease patients by leveraging AI agents to analyze clinical phenotypes, genomic data, and medical literature in a transparent, interpretable manner.

Rare diseases affect more than 300 million people worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged 'diagnostic odyssey' exceeding 5 years, marked by repeated referrals, misdiagnoses and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burden. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support with traceable reasoning.

A natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation for Mendelian diseases via structured tool access (MCP). MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. A 20B-parameter model achieved 94% pass rate on 100 expert-curated questions (vs 41% without MARRVEL-MCP), approaching state-of-the-art proprietary performance. Establishes context engineering as a core principle for biomedical AI.

Open-source, local-first autonomous AI assistant: file/shell access, browser automation, skills/plugins, and optional chat-app bridges; model-agnostic (e.g. Claude, OpenAI, local/Ollama) with your own API keys.

Robin is the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate testable hypotheses from literature and design experiments to validate them, automating the entire scientific discovery cycle for biological research.

Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate testable hypotheses from literature and design experiments to validate them.

SciSciGPT is an open-source, prototype AI collaborator that uses the domain of science of science as a testbed to explore LLM-powered scientific collaboration. It assists researchers in analyzing scientific literature, identifying research trends, generating hypotheses about scientific dynamics, and facilitating human-AI collaborative research in metascience and scientometrics.

We introduce SciSciGPT, an open-source, prototype artificial intelligence (AI) collaborator that uses the domain of science of science as a testbed to explore the potential of large language model-powered scientific collaboration. SciSciGPT assists researchers in analyzing scientific literature, identifying research trends, generating hypotheses about scientific dynamics, and facilitating human-AI collaborative research in metascience and scientometrics.

SciToolAgent is a knowledge-graph-driven scientific agent that integrates multiple computational tools for scientific research. It leverages a knowledge graph to understand tool capabilities and relationships, enabling automated multi-tool workflow composition for complex scientific tasks across domains including bioinformatics, cheminformatics, and materials science.

Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools requires substantial domain expertise. While large language models show promise in tool use, they struggle with complex multi-tool workflows. Here we introduce SciToolAgent, a knowledge-graph-driven scientific agent that integrates multiple computational tools for scientific research. By leveraging a knowledge graph to understand tool capabilities and relationships, SciToolAgent enables automated multi-tool workflow composition for complex scientific tasks.

The Virtual Lab is an AI agent framework that uses LLM-powered researchers in a simulated laboratory environment to collaboratively design and test scientific hypotheses. It was demonstrated by successfully designing new SARS-CoV-2 nanobodies, with AI agents specializing in different scientific roles working together to propose, evaluate, and refine experimental designs.

Science frequently benefits from teams of interdisciplinary researchers, but many scientists do not have easy access to experts from multiple fields. Although large language models (LLMs) have shown an impressive ability to aid researchers across diverse domains, their uses have been largely limited to answering specific scientific questions rather than performing open-ended research. Here we expand the capabilities of LLMs for science by introducing the Virtual Lab, a framework where LLM-powered AI agents collaborate in a simulated laboratory to design and test scientific hypotheses. The Virtual Lab successfully designed new SARS-CoV-2 nanobodies, demonstrating the potential of multi-agent AI systems for open-ended scientific discovery.