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

10.6K
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...
10.6K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.5K
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...
1.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

178
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
178
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

347
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
347
Drug Clearance: Overview01:06

Drug Clearance: Overview

270
Drug elimination refers to drug removal from the body, either through urine or bile, by the kidneys or liver, respectively. A pharmacokinetic parameter, drug clearance, measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the drug's concentration in plasma.
Drug clearance is not limited to renal excretion but encompasses all organs involved in drug elimination, including...
270
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.6K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Social and Behavioral Correlates of Self-Perceived Psychological Distress in Celiac Disease During the COVID-19 Pandemic: An Exploratory Cross-Sectional Study (COVIMPACT).

Nutrients·2026
Same author

Elevated microbially-derived metabolites in autism: a possible diagnostic screening test for a distinct ASD phenotype.

Molecular psychiatry·2026
Same author

Evaluating Open and Accessible Visual Language Models for Optical Character Recognition in Clinical Case Report Forms.

Studies in health technology and informatics·2026
Same author

Do predictors of motor recovery differ between robotic and conventional post-stroke rehabilitation?

Journal of neuroengineering and rehabilitation·2026
Same author

Profiling the Athletes' Gut Microbiome: A Critical Methodological Perspective on 16S Metabarcoding and Shotgun Metagenomics.

Biology·2026
Same author

Endovascular profiles linked to neutrophil activation in children and young adults with long COVID.

Pediatric research·2026

Related Experiment Video

Updated: Nov 30, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.7K

New Perspectives on Machine Learning in Drug Discovery.

Simona Musella1, Giulio Verna1, Alessio Fasano1

  • 1European Biomedical Research Institute of Salerno (EBRIS), Salerno, Italy.

Current Medicinal Chemistry
|November 12, 2020
PubMed
Summary

Artificial intelligence (AI) and machine learning accelerate drug discovery. These methods analyze vast datasets for novel drug candidates and therapies, identifying patterns from structural design through clinical trials.

Keywords:
ADMETMachine learningartificial intelligencedrug design.drug repurposingpersonalized medicinesynthesis planningvirtual screening

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

3.3K
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

10.0K

Related Experiment Videos

Last Updated: Nov 30, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.7K
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

3.3K
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

10.0K

Area of Science:

  • Computational chemistry
  • Pharmacology
  • Biotechnology

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly integral to modern drug development.
  • The availability of large datasets for drug candidates fuels AI/ML applications.
  • Complex data interpretation is key to identifying novel patterns in drug discovery.

Purpose of the Study:

  • To review recent AI/ML applications in drug discovery and therapies.
  • To analyze the limitations of current AI/ML approaches in this field.
  • To discuss future perspectives for AI/ML in pharmaceutical research.

Main Methods:

  • Literature review of recent studies on AI/ML in drug discovery.
  • Assessment of AI/ML applications across various stages of drug development.
  • Analysis of reported successes, challenges, and future trends.

Main Results:

  • AI/ML significantly impacts drug discovery from molecular design to clinical trials.
  • These methods enhance computer-aided drug discovery by interpreting complex datasets.
  • Identified patterns aid in the selection and development of drug candidates.

Conclusions:

  • AI/ML is a transformative technology in drug discovery and development.
  • Addressing current limitations will unlock further potential in therapeutic advancements.
  • Future research should focus on refining AI/ML models for greater precision and efficiency.