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

810
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...
810
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.1K
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...
8.1K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

189
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...
189
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

101
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...
101
Drug Administration and Therapy Phases: Overview01:26

Drug Administration and Therapy Phases: Overview

581
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...
581
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

143
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
143

You might also read

Related Articles

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

Sort by
Same author

Predicting First-in-Human Pharmacokinetics: Comparative Evaluation of Standard PBPK, High-Throughput PBPK, and Machine Learning.

Molecular pharmaceutics·2026
Same author

Applying Deep-Learning-Driven <i>De Novo</i> Design to Hit Identification: A Case Study on A<sub>2A</sub> Adenosine Receptor Antagonists.

Journal of medicinal chemistry·2026
Same author

Structuring Disorder via Supervised Molecular Dynamics: Uncovering Arginine-Glycine-Glycine-Mediated Ribonucleic Acid-Intrinsically Disordered Region Recognition Mechanisms.

Journal of chemical information and modeling·2026
Same author

MolVE: An Open-Source Web Platform for Visualizing and Evaluating AI-Designed Molecules to Aid in Prioritization.

Journal of chemical information and modeling·2026
Same author

High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a Large <i>In Vivo</i> Pharmacokinetics Data Set.

Molecular pharmaceutics·2026
Same author

MDM2 in Tumor Biology and Cancer Therapy: A Review of Current Clinical Trials.

International journal of molecular sciences·2026

Related Experiment Video

Updated: Jul 30, 2025

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

408

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.

Davide Bassani1,2, Stefano Moro2

  • 1Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland.

Molecules (Basel, Switzerland)
|May 13, 2023
PubMed
Summary

Computer-aided drug design (CADD) accelerates pharmaceutical discovery by enabling rational compound selection and molecular-level understanding. This review covers CADD methods, their benefits, and limitations in drug development.

Keywords:
AICADDchemistrycomputationaldesigndruglearningmolecular dockingmolecular dynamics

More Related Videos

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

9.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

4.9K

Related Experiment Videos

Last Updated: Jul 30, 2025

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

408
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

9.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

4.9K

Area of Science:

  • Computational chemistry and pharmacology
  • Drug discovery and development

Background:

  • Computer-aided drug design (CADD) is integral to modern pharmaceutical pipelines.
  • CADD enhances the efficiency and rationality of identifying drug candidates.
  • It enables molecular-level understanding of biological interactions.

Purpose of the Study:

  • To review state-of-the-art CADD methods.
  • To highlight CADD applications in pharmaceutical and biological research.
  • To discuss the potential, benefits, limitations, and weaknesses of CADD.

Main Methods:

  • Review of current literature on computer-aided drug design techniques.
  • Analysis of CADD applications across various pharmaceutical and biological scenarios.
  • Discussion of computational approaches for rational 3D drug design and optimization.

Main Results:

  • CADD significantly speeds up early drug discovery stages.
  • It facilitates rational selection of compounds from vast chemical spaces.
  • Computational tools enable rationalization of biochemical processes at the molecular level.

Conclusions:

  • CADD is a cornerstone of modern drug discovery, offering significant advantages.
  • The review provides an overview of CADD's potential and current challenges.
  • Further advancements in CADD are crucial for efficient pharmaceutical innovation.