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

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

You might also read

Related Articles

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

Sort by

Same author

Chimeric kinase ALK induces expression of NAMPT and selectively depends on this metabolic enzyme to sustain its own oncogenic function.

Leukemia·2023

Same author

The AACR Journals: Advancing Progress Toward the AACR's 115-Year Mission.

Molecular cancer research : MCR·2022

Same author

The AACR Journals: Advancing Progress Toward the AACR's 115-Year Mission.

Molecular cancer therapeutics·2022

Same author

The AACR Journals: Advancing Progress Toward the AACR's 115-Year Mission.

Cancer research·2022

Same author

The AACR Journals: Advancing Progress Toward the AACR's 115-Year Mission.

Blood cancer discovery·2022

Same author

The AACR Journals: Advancing Progress Toward the AACR's 115-Year Mission.

Cancer immunology research·2022

Same journal

Canonical and phosphoribosyl ubiquitination coordinate to stabilize a proteinaceous structure surrounding the <i>Legionella</i>-containing vacuole.

eLife·2026

Same journal

Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.

eLife·2026

Same journal

Dynamic assembly of malate dehydrogenase-citrate synthase multienzyme complex in the mitochondria.

eLife·2026

Same journal

Autosomal allelic inactivation at loci with variable replication timing and dosage sensitivity.

eLife·2026

Same journal

Cribriform plate microenvironment assembles a suppressive myeloid network during EAE-induced neuroinflammation.

eLife·2026

Same journal

Proteomic composition and mutual assembly of the C2a projection in vertebrate motile cilia.

eLife·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Mixed outcomes for computational predictions.

¹Abramson Cancer Center, Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, United States.

|January 20, 2017

Summary

This summary is machine-generated.

Validating a computational model for drug repurposing revealed the intricate complexity of cancer biology. This highlights challenges in predicting new therapeutic applications for existing medications.

Keywords:

Reproducibility Project: Cancer Biology cancer biology cimetidine drug retargeting human metascience replication reproducibility

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Mar 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Oncology
Computational Biology
Pharmacology

Background:

Computational models are increasingly used to predict novel therapeutic applications for existing drugs.
Drug repurposing offers a potentially faster and more cost-effective approach to developing new cancer treatments.
Validating these computational predictions experimentally is crucial for clinical translation.

Purpose of the Study:

To experimentally validate the predictions of a computational model designed for identifying new uses of existing drugs.
To assess the feasibility and challenges associated with computational drug repurposing in oncology.
To understand the complexities of cancer biology that influence the success of drug repurposing strategies.

Main Methods:

Development and application of a computational model for drug repurposing.
Selection of candidate drugs and predicted new uses based on model output.
Experimental validation of predicted drug efficacy in relevant cancer models.
Analysis of biological mechanisms underlying drug response or lack thereof.

Main Results:

Experimental validation efforts encountered significant challenges.
The inherent complexity of cancer biology complicated the direct translation of computational predictions.
Observed outcomes highlighted the need for nuanced understanding beyond initial model predictions.

Conclusions:

Experimental validation is essential but complex when assessing computational drug repurposing predictions.
Cancer biology presents multifaceted challenges that require sophisticated approaches for effective drug repurposing.
Future efforts must integrate deeper biological insights with computational strategies for successful oncology drug discovery.