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

12.5K
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...
12.5K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.9K
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...
1.9K
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

64
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
64
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

59
PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
59
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

301
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...
301
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

57
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
57

You might also read

Related Articles

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

Sort by
Same author

Cell painting and thermal proteome profiling for inference of drug targets and mechanism of action.

Molecular systems biology·2026
Same author

Counting cells can accurately predict small-molecule bioactivity benchmarks.

Nature communications·2026
Same author

Precise mapping of single-stranded DNA breaks by sequence-templated erroneous DNA polymerase end-labelling.

Nature communications·2025
Same author

Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis.

NPJ digital medicine·2025
Same author

Computational drug repurposing: approaches, evaluation of in silico resources and case studies.

Nature reviews. Drug discovery·2025
Same author

Associations of PFAS and OH-PCBs with risk of multiple sclerosis onset and disability worsening.

Nature communications·2025

Related Experiment Video

Updated: Mar 10, 2026

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

10.3K

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Samuel Lampa1, Jonathan Alvarsson1, Ola Spjuth2

  • 1Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden.

Journal of Cheminformatics
|December 13, 2016
PubMed
Summary

Automating predictive modeling for drug discovery is complex. SciLuigi, an extension of the Luigi system, enhances workflow management for agile, flexible, and fault-tolerant automation of complex computational tasks.

Keywords:
Drug discoveryFlow-based programmingMachine learningPredictive modellingWorkflows

More Related Videos

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

1.4K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

12.0K

Related Experiment Videos

Last Updated: Mar 10, 2026

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

10.3K
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

1.4K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

12.0K

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Automating predictive modeling in drug discovery is challenging due to complex dependencies and computational demands.
  • Existing workflow management systems lack the flexibility for agile and adaptable predictive modeling.
  • Large-scale data and demanding methods necessitate robust e-infrastructures like high-performance computing.

Purpose of the Study:

  • To present SciLuigi, a novel approach for automating predictive modeling in drug discovery.
  • To address the limitations of current workflow systems in handling complex modeling tasks.
  • To enable agile and flexible automation of computational drug discovery pipelines.

Main Methods:

  • Implementation of SciLuigi as an extension of the Luigi system, inspired by flow-based programming.
  • Utilizing a shared computer cluster for large-scale biochemical interaction modeling.
  • Developing a system to manage complex dependencies, cross-validation, and parameter tuning in predictive modeling.

Main Results:

  • SciLuigi facilitates the automation of complex, multi-step predictive modeling tasks.
  • The approach demonstrated effectiveness in modeling large sets of biochemical interactions.
  • Experiences highlight improved flexibility and fault tolerance in computational drug discovery workflows.

Conclusions:

  • SciLuigi offers a promising solution for enhancing automation in predictive drug discovery.
  • The flow-based programming approach integrated into SciLuigi improves workflow management.
  • This system supports the efficient handling of large-scale data and complex computational models in drug discovery research.