Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

10.9K
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...
10.9K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.7K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.3K
An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
5.3K
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

1.2K
Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...
1.2K
Preclinical Development: Overview01:28

Preclinical Development: Overview

5.8K
Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
5.8K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.1K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.

Journal of cheminformatics·2026
Same author

Research progress on active ingredients of traditional Chinese medicine in the treatment of asthenozoospermia.

Frontiers in reproductive health·2026
Same author

SynGFN: learning across chemical space with generative flow-based molecular discovery.

Nature computational science·2025
Same author

Dynamic clinical trial success rates for drugs in the 21st century.

Nature communications·2025
Same author

PepBAN: A Deep Learning Framework with Bilinear Attention and Adversarial Learning for Peptide-Protein Interaction Prediction.

Journal of chemical information and modeling·2025
Same author

Towards the elimination of infectious HPV: exploiting CRISPR/Cas innovations.

Frontiers in cellular and infection microbiology·2025

Related Experiment Video

Updated: Jan 13, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

786

[Application and progress of artificial intelligence agents in drug development].

Donghai Zhao1, Changyu Hsieh2

  • 1School of Pharmacy, Zhejiang University, Hangzhou 310058, China. 22319109@zju.edu.cn.

Zhejiang Da Xue Xue Bao. Yi Xue Ban = Journal of Zhejiang University. Medical Sciences
|January 6, 2026
PubMed
Summary

Artificial intelligence (AI) agents, powered by large language models, are revolutionizing drug discovery by acting as active collaborators. These AI agents enhance efficiency, improve prediction accuracy, and reduce risks throughout the drug development pipeline.

Keywords:
Artificial intelligence agentDrug developmentLarge language modelMulti-agent systemReview

More Related Videos

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Related Experiment Videos

Last Updated: Jan 13, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

786
Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

3.6K
Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
09:11

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

2.7K

Area of Science:

  • Biomedical research
  • Computational chemistry
  • Artificial intelligence in medicine

Background:

  • Drug discovery faces challenges like high costs, risks, and long timelines.
  • Emerging artificial intelligence (AI) agents offer potential solutions.
  • AI agents transition from tools to active collaborators in research.

Purpose of the Study:

  • To systematically review AI agents' architecture and applications in drug development.
  • To highlight AI's role in improving efficiency and mitigating risks.
  • To provide a reference for researchers in AI-driven drug discovery.

Main Methods:

  • Review of AI agent capabilities, including autonomous reasoning and tool utilization.
  • Analysis of AI applications across drug discovery stages: target identification, molecular design, synthesis planning.
  • Discussion of AI integration with automated experimental platforms.

Main Results:

  • AI agents can identify novel targets and mechanisms through knowledge integration.
  • Automation of complex tasks like molecular design and synthesis planning is feasible.
  • AI agents facilitate closing the loop from virtual design to physical experimentation.

Conclusions:

  • AI agents represent a paradigm shift in drug discovery, moving towards integrated platforms and general-purpose biomedical agents.
  • Further development is needed to address current limitations and unlock full potential.
  • AI agents are poised to significantly transform the efficiency and success rate of drug development.