Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.

Communications chemistry·2026

Same author

Highly selective and AI-predictable Se-N exchange chemistry between benzoselenazolones and boronic acids for programmable, parallel, and DNA-encoded library synthesis.

Chemical science·2025

Same author

CycleDesigner: Leveraging CycRFdiffusion and HighFold to Design Cyclic Peptide Binders for Specific Targets.

Journal of chemical information and modeling·2025

Same author

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation.

Journal of chemical theory and computation·2025

Same author

Homogeneous continuous flow nitration of <i>O</i>-methylisouronium sulfate and its optimization by kinetic modeling.

Beilstein journal of organic chemistry·2024

Same author

Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data.

Journal of cheminformatics·2024

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large

Ziyang Gong¹, Chengwei Zhang², Danyang Song²

¹Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, National Engineering Research Center for Process Development of Active Pharmaceutical Ingredients, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, P. R. China.

Chemical Communications (Cambridge, England)

|August 1, 2025

Summary

This summary is machine-generated.

ChemReactSeek automates chemical reaction protocol design using artificial intelligence and large language models (LLMs). This platform extracts data from literature to generate and experimentally validate hydrogenation reaction conditions.

More Related Videos

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Sep 13, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Chemical synthesis
Artificial intelligence in chemistry
Reaction engineering

Background:

Designing chemical reaction protocols is complex and time-consuming.
Automating protocol design can accelerate chemical research and development.

Purpose of the Study:

To introduce ChemReactSeek, an AI platform for automating chemical reaction protocol design.
To leverage retrieval-augmented generation with large language models (LLMs) for this purpose.

Main Methods:

Utilizing DeepSeek-v3 for data extraction and structuring from scientific literature.
Building a specialized knowledge base for hydrogenation reactions.
Employing FAISS-based semantic search and LLM-driven reasoning.
Experimental validation of generated protocols for heterogeneous hydrogenation.

Main Results:

ChemReactSeek successfully extracts and structures relevant chemical reaction data.
The platform generates executable reaction conditions for hydrogenation.
Experimental validation confirms the efficacy of the designed protocols.

Conclusions:

ChemReactSeek demonstrates a novel approach to automating chemical reaction protocol design.
The integration of LLMs and semantic search offers a powerful tool for synthetic chemists.
This AI-driven platform has the potential to significantly expedite chemical synthesis research.