Autonomous chemical research with large language models

Checked 30 Sept 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing 1–5 , biology 6,7 , chemistry 8–10 and computer programming 11,12 . Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research.

• Independent teams have converged on multi-agent, tool-using LLM architectures as the core design pattern for AI-for-science systems: Coscientist (Boiko et al. 2023) pairs GPT-4 with search, code execution and lab automation, Co-Scientist (Gottweis et al. 2026) is a multi-agent system built on Gemini, and the Virtual Lab (Swanson et al. 2024) has an LLM principal investigator guiding specialist agents. Qi et al. 2026's survey argues this convergence is functional, since multi-agent redundancy offers error-checking that can mitigate hallucination and bias. Trace →
• Agent-driven pipelines have already produced experimentally validated wet-lab results in more than one domain: Boiko et al. 2023's Coscientist successfully optimized palladium-catalysed cross-coupling reactions, and Swanson et al. 2024's Virtual Lab designed 92 nanobodies, two of which showed improved binding to recent SARS-CoV-2 variants while retaining binding to the ancestral spike. Trace →
• AI agents have already produced experimentally validated results in multiple domains: Boiko et al. 2023's Coscientist optimized palladium-catalysed cross-couplings, Ghareeb et al. 2026's Robin identified and confirmed ripasudil for dry age-related macular degeneration in vitro, and Swanson et al. 2024's Virtual Lab produced nanobodies with validated binding across SARS-CoV-2 variants. Trace →
• Li et al. 2025 explicitly periodize the field, naming Boiko et al. 2023's Coscientist and Huang et al. 2025's Biomni as markers of a 'scientific-agent phase' beginning in 2023 — framing these independently developed systems as milestones of a single transition to autonomous experiment design and workflow iteration. Trace →

• AI for science and agentic researchanchor