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

Antibiotic Selection

Antibiotic Selection

Development of Antibiotic Resistance

Development of Antibiotic Resistance

Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...

Microorganisms in Medicine and Therapeutics

Microorganisms in Medicine and Therapeutics

Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.

Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

You might also read

Related Articles

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

Sort by

Same author

SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.

Molecular systems biology·2026

Same author

Focusing on Data to Improve Machine Learning-Guided Antibiotic Discovery.

Microbial drug resistance (Larchmont, N.Y.)·2026

Same author

Call for Papers: Artificial Intelligence for Next-generation Anti-infective Discovery.

ACS infectious diseases·2026

Same author

AI-guided molecular design with recipes included.

Nature computational science·2025

Same author

Author Correction: Discovery and artificial intelligence-guided mechanistic elucidation of a narrow-spectrum antibiotic.

Nature microbiology·2025

Same author

Discovery and artificial intelligence-guided mechanistic elucidation of a narrow-spectrum antibiotic.

Nature microbiology·2025

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

Antibiotic Dereplication Using the Antibiotic Resistance Platform

Antibiotic Dereplication Using the Antibiotic Resistance Platform

Published on: October 17, 2019

A brief guide to machine learning for antibiotic discovery.

Gary Liu¹, Jonathan M Stokes¹

¹Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, Ontario, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada; David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada.

Current Opinion in Microbiology

|August 13, 2022

Summary

This summary is machine-generated.

Algorithmic approaches, including machine learning and deep learning, accelerate the discovery of new antibiotics. Sharing high-quality screening data is crucial for training advanced antibiotic prediction models.

More Related Videos

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Published on: April 18, 2019

Related Experiment Videos

Last Updated: Sep 1, 2025

Antibiotic Dereplication Using the Antibiotic Resistance Platform

Antibiotic Dereplication Using the Antibiotic Resistance Platform

Published on: October 17, 2019

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

Published on: April 18, 2019

Area of Science:

Microbiology
Computational Chemistry
Drug Discovery

Background:

Antibiotic resistance is a growing global health threat.
The pipeline for new antibiotics is critically insufficient.
Novel strategies are urgently needed to discover new antibiotic classes.

Purpose of the Study:

To review the application of machine learning and deep learning in antibiotic discovery.
To highlight the potential of algorithmic methods in identifying new small-molecule antibiotics and natural products.
To advocate for open data sharing to advance computational antibiotic discovery.

Main Methods:

Review of contemporary machine learning and deep learning models.
Analysis of algorithmic predictions across large chemical spaces.
Discussion of applications in identifying novel antibiotics and natural products.

Main Results:

Algorithmic approaches can predict new antibiotics orders of magnitude faster than traditional screening.
Machine learning and deep learning models show significant utility in guiding antibiotic discovery.
These computational methods enhance the probability of finding antibiotics with desired properties.

Conclusions:

Machine learning and deep learning are powerful tools for modern antibiotic discovery.
Open sharing of high-quality screening datasets is essential to accelerate model training.
Wider adoption of these computational methods can significantly impact the fight against antibiotic resistance.