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

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.6K
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...
1.6K
Drug Discovery: Overview01:26

Drug Discovery: Overview

10.8K
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...
10.8K
Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

175
Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
175
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

1.1K
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...
1.1K
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

5.2K
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...
5.2K
Principles of Drug Action01:24

Principles of Drug Action

7.8K
Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Virtual Tumors Enable Prediction of Personalized Therapeutic Combinations for Non-Small Cell Lung Cancer.

Cancer research·2026
Same author

Toxicological impacts of environmentally equivalent microplastics and cadmium co-exposure in tropical freshwater crab <i>Sartoriana spinigera</i>.

Frontiers in toxicology·2026
Same author

Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.

Nature communications·2026
Same author

How artificial intelligence is reengineering protein engineering.

Science (New York, N.Y.)·2026
Same author

The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.

Journal of chemical information and modeling·2026
Same author

A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data.

Journal of chemical information and modeling·2026

Related Experiment Video

Updated: Jan 2, 2026

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

10.1K

Rethinking drug design in the artificial intelligence era.

Petra Schneider1, W Patrick Walters2, Alleyn T Plowright3

  • 1ETH Zurich, RETHINK, Department of Chemistry and Applied Biosciences, Zurich, Switzerland.

Nature Reviews. Drug Discovery
|December 6, 2019
PubMed
Summary

Artificial intelligence (AI) offers new opportunities and challenges in drug discovery. Experts discuss key hurdles and strategies for integrating AI into small-molecule drug development, balancing potential with current realities.

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

863
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.0K

Related Experiment Videos

Last Updated: Jan 2, 2026

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

10.1K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

863
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.0K

Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Biotechnology

Background:

  • Artificial intelligence (AI) is increasingly utilized in pharmaceutical research and development.
  • Debate exists regarding the tangible impact of AI on drug discovery projects, with some experts optimistic and others awaiting concrete evidence.
  • AI integration presents novel challenges for scientific researchers and the biopharmaceutical industry's established drug development pipelines.

Purpose of the Study:

  • To present expert perspectives on the significant challenges in applying AI to small-molecule drug discovery.
  • To explore potential approaches and strategies for overcoming these identified challenges.
  • To provide a balanced view on the current state and future of AI in medicinal chemistry.

Main Methods:

  • The study compiles insights from a diverse group of international experts in the field.
  • Expert opinions were gathered regarding the 'grand challenges' in AI-driven small-molecule drug discovery.
  • Discussions focused on practical approaches to address the identified challenges.

Main Results:

  • AI presents both significant opportunities and considerable challenges in drug discovery.
  • Key challenges include integrating AI into existing workflows and validating AI-driven results.
  • Expert consensus highlights the need for new strategies and interdisciplinary collaboration.

Conclusions:

  • AI is transforming small-molecule drug discovery, necessitating adaptation within the biopharma industry.
  • Addressing the 'grand challenges' requires innovative approaches and a realistic assessment of AI's current capabilities.
  • Further research and development are crucial to fully realize AI's potential in accelerating medicine development.