ArticleNature machine intelligence2024
Augmenting large language models with chemistry tools.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 218 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
218 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
- ChemGraph-XANES: an agentic framework for XANES simulation and curation.RSC advances · 2026Article
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework.ACS central science · 2026Article
- Molecular Docking of Natural Products: Critical Appraisal of Current Methodology and Practical Guidelines.Molecules (Basel, Switzerland) · 2026Review
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- BONAFIDE: a Python framework for the calculation of local features for atoms and bonds in molecules.Journal of cheminformatics · 2026Article
- Robust out-of-distribution prediction of Buchwald-Hartwig reactions.Nature computational science · 2026Article
- Materials Databases: Foundations of Modern Digital Materials.Precision chemistry · 2026Review
- Turning Immersive Viewers into Analytical Workspaces: ASCRIBE-XR and Agent-Driven Scientific Visualization.Journal of imaging · 2026Article
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra.Analytical chemistry · 2026Article
- BioMaster: Multi-agent system for automated bioinformatics analysis workflow.Patterns (New York, N.Y.) · 2026Article
- Censoring chemical data to mitigate dual use risk.Digital discovery · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- CoSP: Reconfigurable Metamaterial Inverse Design via Contrastive Pretrained Large Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- Automating Chemical Reasoning in High-Throughput Phase Identification With a Probabilistic, LLM-Guided Framework.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SmileyLlama: modifying large language models for directed chemical space exploration.Nature computational science · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
158 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.
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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.