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

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
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
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.1K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.1K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

858
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...
858
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

118
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.
118

You might also read

Related Articles

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

Sort by
Same author

CLC-Pred Synergy: Web Application for Predicting Pairwise Drug Combinations with Synergistic Activity Against NCI60 Cancer Cell Lines.

International journal of molecular sciences·2026
Same author

RNA Therapeutics in Viral Infections and Cancer: Mechanisms, Challenges, and Prospects: A Review.

Pharmaceutics·2026
Same author

An Integrated Text Mining Approach for Discovering Pharmacological Effects, Drug Combinations, and Repurposing Opportunities of ACE Inhibitors.

International journal of molecular sciences·2026
Same author

Docking in the Dark: Insights into Protein-Protein and Protein-Ligand Blind Docking.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

SAR Modeling to Predict Ames Mutagenicity Across Different <i>Salmonella typhimurium</i> Strains.

Pharmaceuticals (Basel, Switzerland)·2025
Same author

QSAR Modeling for Predicting IC<sub>50</sub> and GI<sub>50</sub> Values for Human Cell Lines Used in Toxicological Studies.

International journal of molecular sciences·2025
Same journal

Unlocking the Prognostic Power of the m-CALLY Index in Cardiovascular-Kidney-Metabolic Syndrome.

Current medicinal chemistry·2026
Same journal

Ferritinophagy-Related Genes in Breast Cancer: Insights from Multi-Omics Analysis.

Current medicinal chemistry·2026
Same journal

Research Progress on Natural Products and Synergistic Nanostrategies for Targeting Ferroptosis in Osteosarcoma.

Current medicinal chemistry·2026
Same journal

Revealing Antihypertensive Drugs for Reducing NAFLD Risk: Genetic Evidence from a Mendelian Randomization Study.

Current medicinal chemistry·2026
Same journal

Identification of Diagnostic Biomarkers Related to Oxidative Stress in Rheumatoid Arthritis.

Current medicinal chemistry·2026
Same journal

Mechanistic Insights into Ginkgo Biloba Extract's Anti-Inflammatory Effects in COPD: Regulation of Th1/Th2 Balance via the p38 MAPK Pathway.

Current medicinal chemistry·2026
See all related articles

Related Experiment Video

Updated: Aug 6, 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

433

Exploring Scoring Function Space: Developing Computational Models for Drug Discovery.

Gabriela Bitencourt-Ferreira1, Marcos A Villarreal2, Rodrigo Quiroga2

  • 1Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre-RS, Brazil.

Current Medicinal Chemistry
|March 22, 2023
PubMed
Summary
This summary is machine-generated.

Exploring scoring function space offers a systems-level approach for developing machine learning models to predict drug-protein binding affinity, enhancing drug discovery efficiency.

Keywords:
Scoring function spacedrug discoverymachine learningprotein spaceprotein-ligand interactionssystems biology.

More Related Videos

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
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

Related Experiment Videos

Last Updated: Aug 6, 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

433
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
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

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Scoring function space provides a systems-level framework for predicting drug molecule affinity.
  • This approach is crucial for advancing drug discovery methodologies.

Purpose of the Study:

  • To review the concept of scoring function space.
  • To explore its application in developing machine learning models for protein-ligand binding affinity prediction.

Main Methods:

  • Literature search of PubMed for "scoring function space" related articles.
  • Utilized protein crystallographic structures from the Protein Data Bank (PDB) to represent protein space.

Main Results:

  • Systems-level approaches offer a holistic view of receptor-drug interactions in drug discovery.
  • Scoring function space introduces flexibility by framing drug discovery within mathematical spaces.

Conclusions:

  • The scoring function space concept integrates various drug discovery methods.
  • It facilitates a computational search within this space to identify optimal models for predicting receptor-drug binding affinity.