Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Drug Discovery: Overview

Drug Discovery: Overview

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...

Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

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...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by

Same author

Interdisciplinary Pediatric Long-COVID Care: A Descriptive Study of Interventions and Health-Related Quality of Life.

Open forum infectious diseases·2026

Same author

CHARacterizing Pediatric Integrative Medicine Within a Large Academic Medical Center (CHARM).

Global advances in integrative medicine and health·2025

Same author

Identification of a Novel Indolizine RORγT Inverse Agonist Using the AI-Driven Drug Design Platform.

ACS medicinal chemistry letters·2025

Same author

Modeling Carbon Basicity.

Molecular informatics·2025

Same author

Monoaminergic neurotransmitters are bimodal effectors of tau aggregation.

Science advances·2025

Same author

Impact of SARS-CoV-2 (COVID-19) Infections on Mental Health Diagnoses in Youth With Chronic Illness.

JAACAP open·2024

Search research articles

Related Experiment Video

Updated: Jun 30, 2025

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

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

Published on: December 11, 2016

The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular

Jeremy Jones¹, Robert D Clark², Michael S Lawless³

¹Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA. jeremy.jones@simulations-plus.com.

Journal of Computer-Aided Molecular Design

|March 19, 2024

Summary

Artificial Intelligence-driven Drug Design (AIDD) platform optimizes drug discovery by integrating pharmacokinetic and ADMET predictions with evolutionary algorithms. This approach generates novel, active molecules with improved drug-like properties, accelerating development timelines.

Keywords:

Artificial intelligence De novo drug design Evolutionary algorithms Generative chemistry Molecular modeling Multi-parameter optimization Pareto optimization Pharmacokinetic simulation QSAR QSPR

More Related Videos

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Related Experiment Videos

Last Updated: Jun 30, 2025

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

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

Published on: December 11, 2016

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Computational chemistry
Medicinal chemistry
Drug discovery

Background:

Computer-aided drug design (CADD) has advanced significantly, with in silico designed molecules reaching clinical trials.
Early CADD focused on target affinity; however, pharmacokinetic and ADMET properties are crucial for successful drug development.
Multi-parameter optimization is increasingly integrated into drug design platforms.

Purpose of the Study:

Introduce the Artificial Intelligence-driven Drug Design (AIDD) platform.
Demonstrate AIDD's capability to automate drug design and multi-objective optimization.
Illustrate AIDD's application using Plasmodium falciparum dihydroorotate dehydrogenase inhibitors.

Main Methods:

Integration of high-throughput physiologically-based pharmacokinetic simulations (GastroPlus) and ADMET predictions (ADMET Predictor).
Utilization of an advanced evolutionary algorithm for multi-objective optimization, distinct from current generative models.
Iterative generation of novel molecules based on activity, pharmacokinetic, and ADMET estimations.

Main Results:

The AIDD platform successfully generates novel molecular sets with desired activity and lead-like properties.
The workflow integrates diverse computational tools for comprehensive drug design.
Demonstrated efficacy in generating potential drug candidates for specific targets, such as malaria.

Conclusions:

AIDD offers an automated and efficient approach to multi-parameter drug design.
The platform accelerates the identification of drug candidates with improved pharmacokinetic and safety profiles.
AIDD represents a significant advancement in leveraging AI for rational drug discovery.