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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Related Experiment Video

Updated: May 11, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction.

Abdulelah S Alshehri^1,2, Kai A Horstmann³, Fengqi You¹

¹Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States.

Journal of Chemical Information and Modeling

|July 15, 2024

Summary

This summary is machine-generated.

ChemREL is a new open-source pipeline for extracting chemical data, improving accuracy and enabling models to adapt to new tasks. This advances machine learning for chemical discovery.

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Area of Science:

Computational Chemistry
Bioinformatics
Data Science

Background:

Scientific documents contain valuable chemical information.
Automated extraction methods using machine learning are improving efficiency but lack accuracy and adaptability.
Current pipelines struggle with transferability and extensibility for new chemical data extraction tasks.

Purpose of the Study:

To develop a versatile chemical data extraction pipeline named ChemREL.
To enhance performance, transferability, and extensibility in chemical data extraction.
To enable deep learning-assisted insights and breakthroughs in chemistry.

Main Methods:

ChemREL utilizes a custom, diverse dataset of chemical documents.
An active learning strategy was employed for data labeling.
The pipeline was trained and fine-tuned for extracting normal melting point and lethal dose 50 (LD50).

Main Results:

ChemREL achieved high F1-scores: 96.1% for entity identification, 97.0% for relation mapping, and 95.4% overall.
The pipeline demonstrated strong transferability, effectively transitioning from melting point to LD50 extraction with minimal data.
ChemREL outperformed existing methods and GPT-4 in chemical data extraction tasks.

Conclusions:

ChemREL offers a significant advancement in automated chemical data extraction.
Its performance, transferability, and extensibility facilitate the creation of large relational datasets.
The open-source release of ChemREL aims to accelerate chemical discovery by broadening data access.