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

Hybridoma Technology

Hybridoma Technology

Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
Hybridoma Selection
Commonly used fusion techniques — electroporation,...

You might also read

Related Articles

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

Sort by

Same author

CDH2 marks a fibroblast-associated stromal cell state in pancreatic ductal adenocarcinoma.

Human cell·2026

Same author

The blood glucose trajectories among non-diabetic patients with total joint arthroplasty: clinical characteristics and predictors.

Frontiers in medicine·2026

Same author

Impact of multilevel postpartum family planning interventions on contraceptive use within 1 year after childbirth: a cluster-randomised controlled clinical trial.

BMJ sexual & reproductive health·2026

Same author

HPV epidemiologic study in Bengbu, China, involving 123,808 women, 2014-2023.

Virology journal·2026

Same author

Mapping the scientific landscape of robotic hernia repair: a bibliometric and topic modeling analysis of thematic transitions.

Journal of robotic surgery·2026

Same author

The association between inhaled tire-wear particle exposure and cognitive dysfunction in rats.

Environmental pollution (Barking, Essex : 1987)·2026

Search research articles

Related Experiment Video

Updated: Jun 3, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

The Application of Machine Learning on Antibody Discovery and Optimization.

Jiayao Zheng¹, Yu Wang², Qianying Liang²

¹School of Pharmacy & School of Biological and Food Engineering, Changzhou University, Changzhou 213164, China.

Molecules (Basel, Switzerland)

|January 8, 2025

Summary

Machine learning (ML) models are revolutionizing antibody discovery and optimization. These AI approaches significantly reduce time and cost compared to traditional methods, accelerating the development of new antibody therapeutics.

Keywords:

antibody developability antibody engineering antibody–antigen interaction computational antibody design machine learning

More Related Videos

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

Related Experiment Videos

Last Updated: Jun 3, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Single-cell Screening Method for the Selection and Recovery of Antibodies with Desired Specificities from Enriched Human Memory B Cell Populations

Published on: August 22, 2019

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Generation of Murine Monoclonal Antibodies by Hybridoma Technology

Published on: January 2, 2017

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Antibodies are crucial in medicine for diagnostics and therapeutics.
Traditional antibody discovery is slow and expensive.
Advancements in data, hardware, and ML offer new possibilities.

Purpose of the Study:

To review recent developments in machine learning-based antibody discovery and optimization.
To compare ML methods with traditional approaches.
To explore future directions and ethical considerations.

Main Methods:

Review of current literature on ML in antibody design.
Analysis of ML model capabilities for in silico antibody generation.
Discussion of time and cost efficiencies.

Main Results:

ML models enable rapid, in silico antibody design.
Significant reductions in time (approx. 60%) and cost (approx. 50%) are achievable.
ML shows promise in overcoming limitations of traditional methods.

Conclusions:

Machine learning is transforming antibody discovery and optimization.
Future research should focus on AI agents and data infrastructure.
Ethical and regulatory frameworks are vital for ML adoption.