ArticleNature communications2024
An automatic end-to-end chemical synthesis development platform powered by large language models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- TeLLAgent: a dual-agent framework for reliable scientific discovery with tool-enhanced LLMs.Chemical science · 2026Article
- Advancing battery failure diagnosis by knowledge-augmented large language models.National science review · 2026Article
- Review
- A chemically-aware validation framework for benchmarking large language models in materials synthesis planning.Journal of cheminformatics · 2026Article
- Direct Quantum Yield Measurement of a Ligand-to-Metal Charge Transfer Photocatalyst via Cyclic Voltammetry.Journal of the American Chemical Society · 2026Article
- Unlocking azobenzene isomerization mechanismsChemical science · 2026Article
- MDCrow: automating molecular dynamics workflows with large language models.Machine learning: science and technology · 2026Article
- Digital materials ecosystem: from databases to AI agents for autonomous discovery.Chemical science · 2026Review
- ReactionSeek: LLM-powered literature data mining and knowledge discovery in organic synthesis.Nature communications · 2026Article
- Article
- Medical large language models and systems in the clinical application of spinal diseases: Current status, challenges, and future prospects.Journal of orthopaedic translation · 2026Review
- Antiviral drug discovery and development: challenges and future directions.Signal transduction and targeted therapy · 2026Review
- An LLM-Based Intelligent Agent and Its Application in Making the Lanolin Saponification Process Greener.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A multimodal dataset of causal mechanisms in materials science literature.Scientific data · 2026Article
- Reasoning-agent-driven process simulation, optimization, carbon accounting and decarbonization of distillation.Communications engineering · 2026Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (S
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.