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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Prediction Intervals01:03

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. 
The...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

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...
Predicting Reaction Outcomes02:24

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,...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Related Experiment Videos

Crowd computing: using competitive dynamics to develop and refine highly predictive models.

Jörg Bentzien1, Ingo Muegge, Ben Hamner

  • 1Boehringer Ingelheim Pharmaceuticals, 900 Ridgebury Road, Ridgefield, CT 06877, USA.

Drug Discovery Today
|January 23, 2013
PubMed
Summary
This summary is machine-generated.

Crowd computing platforms like Kaggle™ enhance drug discovery by optimizing in silico models through competitive dynamics. This approach, termed

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Machine learning in drug discovery

Background:

  • Traditional in silico modeling strategies can be time-consuming and may not always yield optimal predictive accuracy.
  • The need for efficient and accurate predictive models is crucial for accelerating the drug discovery pipeline.

Purpose of the Study:

  • To describe the application of a crowd computing platform for developing predictive in silico models.
  • To detail the competitive dynamics inherent in crowd computing platforms and compare them to conventional modeling.
  • To explore the broader utility of 'gamification' in scientific modeling.

Main Methods:

  • Utilized Kaggle™, a crowd computing platform, to foster model optimization through a competitive environment.
  • Disclosed the complete structure of the underlying dataset used for model development.
  • Compared the crowd-sourced modeling dynamic with established, conventional modeling strategies.

Main Results:

  • The competitive dynamic of the crowd computing platform led to significant in silico model optimization.
  • Predictive models developed through this platform demonstrated high accuracy for drug discovery applications.
  • The study provides a detailed comparison of this novel approach against traditional methods.

Conclusions:

  • Crowd computing platforms offer a powerful and effective 'gamified' approach to accelerate and improve in silico model development for drug discovery.
  • The disclosed dataset and methodology provide a foundation for further research into gamified modeling strategies.
  • This approach holds significant promise for broader applications in scientific modeling beyond drug discovery.