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

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Mass Spectrometry: Aromatic Compound Fragmentation

Mass Spectrometry: Aromatic Compound Fragmentation

Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...

You might also read

Related Articles

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

Sort by

Same author

ISL@mTOMV nanoparticles: a promising approach for VM-targeted therapy in breast cancer.

Journal of nanobiotechnology·2026

Same author

Enhanced Antibacterial and Immunomodulatory Porphyrin-Based MOF Coatings for PETG Clear Aligners: A Comparative Study of Ag, Cu, and Ce Metal Centers.

International journal of molecular sciences·2026

Same author

Synergistic Heavy-Atom and Vibronic-Coupling Effects for High-Performance Ionic TADF Emitters: A Theoretical Study.

Journal of computational chemistry·2026

Same author

Genetic variation in antidiabetic drug targets: associations with Parkinson's disease risk and age at onset.

NPJ Parkinson's disease·2026

Same author

Biocontrol Efficacy and Mechanisms of <i>Bacillus velezensis</i> AP6 Against Brown Rot in Yam by <i>Fusarium solani</i>.

Journal of fungi (Basel, Switzerland)·2026

Same author

Multigated DNA Cascade Amplifier for Ultrasensitive Spatiotemporal Imaging of PLS3 mRNA at the Single-Cell Level for Early Detection of Breast Cancer Metastasis.

Analytical chemistry·2026

Same journal

Computational design of low-volatility lubricants for space using interpretable machine learning.

Journal of cheminformatics·2026

Same journal

OpenStats: how to combine statistics and research data management (RDM) to leverage efficient scientific data analysis by guided statistics.

Journal of cheminformatics·2026

Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026

Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026

Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026

Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2025

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

Automated molecular structure segmentation from documents using ChemSAM.

Bowen Tang^1,2, Zhangming Niu^3,4, Xiaofeng Wang³

¹College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.

Journal of Cheminformatics

|March 13, 2024

Summary

This summary is machine-generated.

This study introduces ChemSAM, a deep learning model using Vision Transformers for accurate chemical structure segmentation from documents. It effectively extracts molecular structures, even from low-resolution images, advancing cheminformatics research.

More Related Videos

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

Related Experiment Videos

Last Updated: Jul 1, 2025

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

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

Published on: December 1, 2023

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules

Published on: March 22, 2019

Area of Science:

Cheminformatics
Computer Vision
Artificial Intelligence

Background:

Chemical structure segmentation is crucial for extracting information from scientific documents.
Existing methods may struggle with variations in image quality and complex layouts.

Purpose of the Study:

To develop a deep learning approach for accurate chemical structure segmentation.
To automate the extraction of chemical structures from text-based sources like patents and articles.

Main Methods:

Utilized a Vision Transformer (ViT) based encoder-decoder model for structure detection and pixel-level classification.
Employed mask clustering and post-processing for refining structure segmentation.
Developed the Chemistry-Segment Anything Model (ChemSAM).

Main Results:

ChemSAM achieved state-of-the-art performance on benchmark datasets and real-world tasks.
The model demonstrates robustness against variations in image quality and style.
Successfully extracted chemical structures from low-resolution and densely arranged layouts.

Conclusions:

The deep learning approach effectively automates chemical structure extraction from documents.
ChemSAM obviates the need for handcrafted features, offering a more adaptable solution.
This method enhances the accessibility of structural information within scientific literature.