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

Transformers in Distribution System

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...

You might also read

Related Articles

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

Sort by

Same author

Identification of Heterogeneous Cortical Thickness Patterns Associated with Prenatal Gestational Diabetes Exposure: A SuStaIn-Based Subtyping Study.

bioRxiv : the preprint server for biology·2026

Same author

Prediction of antibody non-specificity using protein language models and biophysical parameters.

mAbs·2026

Same author

<i>In vitro</i> and <i>in vivo</i> inhibition of amyloid β aggregation by a Ru(II)-naphthalene diimide complex.

Dalton transactions (Cambridge, England : 2003)·2026

Same author

Copper and iron co-loaded biochar (CuFe-BC) activated peroxydisulfate (PDS): A novel non-radical system for 4-chlorophenol removal through electron-transfer-induced polymerization.

Journal of environmental management·2026

Same author

Rapid elongation drives the exceptionally fast aggregation of the most common localized human amyloid medin.

Communications chemistry·2026

Same author

Systems-level organization of extracellular proteostasis.

Science (New York, N.Y.)·2026

Same journal

PCSK5 promotes angiogenesis and cardiac repair after myocardial infarction.

Nature communications·2026

Same journal

PfApiAT2 is a proline transporter essential for the transmission of Plasmodium falciparum by the mosquito vector.

Nature communications·2026

Same journal

Transient distortions of the South Atlantic Anomaly radiation environments driven by electric fields.

Nature communications·2026

Same journal

Structural basis of the regulation by CDK11 kinase of early spliceosome activation and evidence for its proofreading by DHX15 helicase.

Nature communications·2026

Same journal

Structural and mechanistic insights into primer synthesis initiation by DNA primase.

Nature communications·2026

Same journal

Changes in heritability and shared environmentality of educational attainment across twentieth-century Norway.

Nature communications·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Sequence-based virtual screening using transformers.

Shengyu Zhang¹, Donghui Huo^1,2, Robert I Horne¹

¹Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.

Nature Communications

|July 28, 2025

Summary

This summary is machine-generated.

Ligand-Transformer, a deep learning method, accurately predicts protein-ligand binding affinity and conformational changes. This approach aids drug discovery by identifying potent inhibitors and understanding molecular interactions.

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Sep 13, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational chemistry
Molecular biology
Drug discovery

Background:

Protein-ligand interactions are crucial for biological processes and drug design.
Deep learning offers a cost-effective alternative to experimental methods for identifying drug candidates.

Purpose of the Study:

Introduce Ligand-Transformer, a novel deep learning method for predicting protein-ligand binding affinity.
Utilize a sequence-based approach to predict molecular interactions and conformational changes.

Main Methods:

Developed Ligand-Transformer, a deep learning model employing the transformer architecture.
Input protein amino acid sequences and small molecule topology to predict binding affinity.
Applied the model to screen EGFR inhibitors and analyze ABL kinase inhibitor effects.

Main Results:

Identified potent inhibitors with low nanomolar potency against mutant EGFR^LTC kinase.
Successfully predicted conformational population shifts induced by ABL kinase inhibitors.
Demonstrated Ligand-Transformer's capability to predict binding affinity and free energy landscape changes.

Conclusions:

Ligand-Transformer accurately predicts protein-small molecule interactions, including binding affinity.
The method facilitates the uncovering of molecular mechanisms and accelerates early-stage drug design.
Sequence-based predictions enable characterization of conformational shifts upon ligand binding.