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...

You might also read

Related Articles

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

Sort by

Same author

Tangential flow filtration for isolating exomeres and other nanoscale extracellular particles.

Nanoscale·2026

Same author

Generation of Cellular Biofactories for the Scalable Production of Surface-Engineered Extracellular Vesicles via CRISPR Genome Editing.

ACS biomaterials science & engineering·2026

Same author

A unified platform for the rapid assembly of glutarimides for Cereblon E3 ligase modulatory drugs.

Nature communications·2026

Same author

Highly conductive and flexible Ag NWs/PBFDO:PEOx composite transparent electrodes enabling efficient interfacial tunneling injection for OLEDs.

Materials horizons·2026

Same author

Cadonilimab plus chemotherapy as first-line treatment in PD-L1-negative advanced non-small cell lung cancer: a phase II clinical trial.

Nature communications·2026

Same author

Use of Human Dose Prediction Metrics to Enable Discovery of AZD3470, an MTA-Cooperative PRMT5 Inhibitor in Clinical Evaluation.

Journal of medicinal chemistry·2026

Search research articles

Related Experiment Video

Updated: Jun 17, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Accelerated Discovery of Carbamate Cbl-b Inhibitors Using Generative AI Models and Structure-Based Drug Design.

Taylor R Quinn¹, Kathryn A Giblin², Clare Thomson²

¹Early TDE Discovery, Oncology R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, Massachusetts 02451, United States.

Journal of Medicinal Chemistry

|August 12, 2024

Summary

Researchers used generative AI and structure-based drug design to discover novel Casitas B-lymphoma proto-oncogene-b (Cbl-b) inhibitors. This accelerated the identification of potent carbamate Cbl-b inhibitors, advancing T cell regulation research.

More Related Videos

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

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

Published on: May 16, 2021

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Related Experiment Videos

Last Updated: Jun 17, 2025

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

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

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

Published on: May 16, 2021

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Area of Science:

Immunology
Medicinal Chemistry
Artificial Intelligence in Drug Discovery

Background:

Casitas B-lymphoma proto-oncogene-b (Cbl-b) is a key E3 ligase regulating T cell, NK cell, and B cell activation.
Cbl-b acts as a negative regulator, making it a target for modulating immune responses.

Purpose of the Study:

To discover novel inhibitors of Cbl-b using a combination of generative AI and structure-based drug design.
To accelerate the drug discovery process for Cbl-b inhibitors.

Main Methods:

Integration of the REINVENT generative AI engine with medicinal chemistry structure-based design.
Iterative in silico structure-based drug design within the Design-Make-Test-Analyze cycle.
Utilized physics-based affinity prediction and machine learning DMPK models to guide design.

Main Results:

Successfully discovered a potent series of carbamate Cbl-b inhibitors.
Demonstrated accelerated discovery through the integrated AI and structure-based design approach.
Validated the effectiveness of in silico predictive models in guiding synthesis selection.

Conclusions:

The combined generative AI and structure-based design approach significantly accelerates the discovery of novel drug candidates.
This strategy efficiently identified potent Cbl-b inhibitors, highlighting its potential for future drug discovery efforts.
Optimized design phase improves efficiency and success rate in identifying targeted molecular inhibitors.