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

1.4K
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...
1.4K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

224
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.
224
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

14.5K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
14.5K
Ligand Binding Sites02:40

Ligand Binding Sites

14.5K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
14.5K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.2K
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...
14.2K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Scaffold-based evaluation metrics for fair comparison of molecular generators.

Journal of cheminformatics·2026
Same author

Artificial Intelligence for Regulatory Evidence: A Systematic Document Analysis of European Medicines Agency Regulatory Advice and Public Reports.

Clinical pharmacology and therapeutics·2026
Same author

A Structure-Based Platform for Predicting Chemical-Induced Parkinson's Disease.

Chemical research in toxicology·2026
Same author

Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site.

Journal of chemical information and modeling·2026
Same author

The Concise Guide to PHARMACOLOGY 2025/26: G protein-coupled receptors.

British journal of pharmacology·2025
Same author

Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling.

Journal of chemical information and modeling·2025
Same journal

Advances in virtual screening.

Drug discovery today. Technologies·2024
Same journal

Integration of virtual and physical screening.

Drug discovery today. Technologies·2024
Same journal

A technical overview of supercritical fluid chromatography-mass spectrometry (SFC-MS) and its recent applications in pharmaceutical research and development.

Drug discovery today. Technologies·2021
Same journal

Mass spectrometry based approaches and strategies in bioanalysis for qualitative and quantitative analysis of pharmaceutically relevant molecules.

Drug discovery today. Technologies·2021
Same journal

Drug discovery from natural products using affinity selection-mass spectrometry.

Drug discovery today. Technologies·2021
Same journal

Editorial for the role(s) of MS in drug research and development.

Drug discovery today. Technologies·2021
See all related articles

Related Experiment Video

Updated: Nov 23, 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

802

Proteochemometrics - recent developments in bioactivity and selectivity modeling.

Brandon J Bongers1, Adriaan P IJzerman1, Gerard J P Van Westen1

  • 1Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.

Drug Discovery Today. Technologies
|January 2, 2021
PubMed
Summary
This summary is machine-generated.

Proteochemometrics, a machine learning method, enhances early drug discovery by predicting ligand-target interactions. This approach improves multi-target modeling and computational drug design efficiency.

More Related Videos

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

842
2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
08:23

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Published on: August 6, 2018

11.7K

Related Experiment Videos

Last Updated: Nov 23, 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

802
Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro
05:50

Pharmacophore Modeling for Targets with Extensive Ligand Libraries: A Case Study on SARS-CoV-2 Mpro

Published on: September 26, 2025

842
2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes
08:23

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Published on: August 6, 2018

11.7K

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Proteochemometrics (PCM) is a machine learning (ML) approach utilizing both ligand and protein descriptors.
  • Its application is growing in early drug discovery, particularly for predicting ligand-target binding.
  • Advancements in ML and data availability are driving PCM's increased use.

Purpose of the Study:

  • To highlight the utility of proteochemometrics in drug discovery.
  • To showcase its application in ligand-target binding prediction and multi-target bioactivity modeling.
  • To demonstrate its role in supporting the design-make-test cycle.

Main Methods:

  • Utilizing a combination of ligand and protein descriptors within a machine learning framework.
  • Developing quantitative structure-activity relationship (QSAR) models for single targets.
  • Applying PCM to protein selectivity, promiscuity, and large-scale deep learning models.

Main Results:

  • Improved predictive power in multi-target bioactivity modeling.
  • Enhanced performance in ligand-target binding predictions.
  • Facilitation of more extensive studies across entire protein families.

Conclusions:

  • Proteochemometrics significantly boosts predictive accuracy in drug discovery.
  • It enables faster, higher-quality computational models for the drug design cycle.
  • The technique supports broader investigations into protein families and complex biological systems.