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

10.3K
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...
10.3K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.4K
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...
1.4K
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

2.5K
The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
2.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.1K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
14.1K
Protein Networks02:26

Protein Networks

4.3K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.3K
G Protein-coupled Receptors01:15

G Protein-coupled Receptors

14.8K
G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
14.8K

You might also read

Related Articles

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

Sort by
Same journal

Radiolysis in drug discovery: molecular modification, repurposing, and mechanistic insights.

Expert opinion on drug discovery·2026
Same journal

Why next-generation mechanistic models will transform drug discovery: integrating efficacy and safety.

Expert opinion on drug discovery·2026
Same journal

Toward a nuanced framework for the medical development of ibogaine and its analogues and derivatives: implications for psychopharmacology.

Expert opinion on drug discovery·2026
Same journal

From mitochondrial signal to discovery decision: a reserve-demand framework for translational risk assessment.

Expert opinion on drug discovery·2026
Same journal

Advances in 3D cell culture models for alzheimer's disease drug discovery.

Expert opinion on drug discovery·2026
Same journal

Are patient-derived models of amyotrophic lateral sclerosis a game changer for novel drug discovery?

Expert opinion on drug discovery·2026

Related Experiment Video

Updated: Nov 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Knowledge graphs and their applications in drug discovery.

Finlay MacLean1

  • 1Target Identification., BenevolentAI, United Kingdom of Great Britain and Northern Ireland.

Expert Opinion on Drug Discovery
|April 12, 2021
PubMed
Summary

Knowledge graphs show promise for biomedical data analysis and drug discovery, particularly in target identification and drug repurposing. However, biases in data and algorithms must be addressed for them to reach their full potential.

Keywords:
Biomedical knowledge graphsdrug repositioningdrug repurposinggraph machine learningheterogeneous information networksknowledge graph embeddingnetwork embeddingsnetwork medicinenetwork pharmacology

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

864
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.9K

Related Experiment Videos

Last Updated: Nov 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

864
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.9K

Area of Science:

  • Biomedical informatics
  • Systems biology
  • Drug discovery

Background:

  • Knowledge graphs are effective for storing and retrieving biomedical information.
  • The increasing volume of multimodal biomedical data and the shift to systems biology favor knowledge graph applications.
  • Knowledge graphs facilitate data storage and hypothesis generation in biomedicine.

Purpose of the Study:

  • To review the applications of knowledge graphs in drug discovery.
  • To evaluate the utility of knowledge graphs beyond theoretical exercises, focusing on practical insights.
  • To highlight key areas like target identification and drug repurposing.

Main Methods:

  • Review of knowledge graph applications in drug discovery.
  • Evaluation of knowledge graph utility and limitations.
  • Case study on COVID-19 drug repurposing using knowledge graphs.
  • Analysis of biases (degree and literature) and mitigation strategies.

Main Results:

  • Knowledge graphs show particular promise in target identification and drug repurposing.
  • A case study demonstrated the use of knowledge graphs to identify repurposable drug candidates for COVID-19.
  • Dangers of degree and literature bias in knowledge graphs were identified, with mitigation strategies discussed.

Conclusions:

  • Knowledge graphs and graph-based machine learning are promising but immature technologies in drug discovery.
  • Current link prediction algorithms often fail to account for biomedical data biases and do not model causal relationships.
  • Addressing data biases and improving causal relationship modeling are crucial for realizing the full potential of knowledge graphs in drug discovery.