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

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.6K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

827
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...
827
Drug Discovery: Overview01:26

Drug Discovery: Overview

8.2K
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...
8.2K

You might also read

Related Articles

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

Sort by
Same author

X-ray Crystallography-Guided Design and Synthesis of Cyclopentyl Heteroaryl Carboxylic Acid-Based Inhibitors of the SARS-CoV-2 Nsp3 Macrodomain (Mac1).

Journal of medicinal chemistry·2026
Same author

Linking biochemical and cellular efficacy of MERS coronavirus main protease inhibitors.

ACS pharmacology & translational science·2026
Same author

Targeted Protein Degradation of NUDT5 Dissociates Catalytic Inhibition from Protein Loss in 6-Thioguanine Response.

Nature communications·2026
Same author

Structure-Guided Discovery of Selective Polo-Like Kinase 3 Inhibitors.

ACS medicinal chemistry letters·2026
Same author

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

Nature communications·2026
Same author

Fragment-Based Design of Targeted Covalent Inhibitors: The Scope and Limitation of Linking Approaches.

ChemMedChem·2026

Related Experiment Video

Updated: Aug 8, 2025

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

192

Turning high-throughput structural biology into predictive inhibitor design.

Kadi L Saar1,2, William McCorkindale1, Daren Fearon3

  • 1Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.

Proceedings of the National Academy of Sciences of the United States of America
|March 6, 2023
PubMed
Summary

Researchers developed a machine learning framework to predict protein-ligand affinity from structural data. This approach accelerates drug design by guiding chemical modifications for enhanced target binding and improved antiviral efficacy.

Keywords:
crystallographydrug designmachine learning

More Related Videos

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

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

Published on: December 1, 2020

5.1K
Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
10:33

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

11.4K

Related Experiment Videos

Last Updated: Aug 8, 2025

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

192
Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

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

Published on: December 1, 2020

5.1K
Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
10:33

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

11.4K

Area of Science:

  • Structural biology
  • Computational chemistry
  • Drug discovery

Background:

  • Drug design often struggles to identify modifications that enhance ligand-protein affinity.
  • High-throughput structural biology, particularly X-ray crystallography, now offers unprecedented data generation capabilities.
  • A framework is needed to translate this structural data into predictive models for rational ligand design.

Purpose of the Study:

  • To develop a machine learning approach for predicting protein-ligand affinity using experimental structural data.
  • To create a predictive model that guides the design of novel ligands with improved binding characteristics.
  • To demonstrate the utility of this framework in accelerating the discovery of potent antiviral agents.

Main Methods:

  • Employed a machine learning model utilizing physics-based energy descriptors for protein-ligand complexes.
  • Implemented a learning-to-rank approach to infer binding mode differences from structural data.
  • Conducted a high-throughput crystallography campaign against SARS-CoV-2 main protease (MPro) with over 200 ligands.
  • Paired structural data with biochemical measurements of binding activity.

Main Results:

  • Successfully predicted protein-ligand affinity from experimental structures and binding data.
  • Designed and synthesized libraries leading to a >10-fold improvement in potency for micromolar hits.
  • Developed a noncovalent, nonpeptidomimetic inhibitor with 120 nM antiviral efficacy against SARS-CoV-2 MPro.
  • Demonstrated successful exploration of unexplored binding pocket regions, expanding chemical space.

Conclusions:

  • The developed machine learning framework effectively translates high-throughput structural data into predictive models for drug design.
  • This approach accelerates the identification of potent drug candidates by enabling efficient exploration of chemical space.
  • The method successfully yielded a promising antiviral inhibitor, showcasing its potential in infectious disease research.