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Related Concept Videos

Drug Discovery: Overview01:26

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

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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...
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Drug administration can occur through various routes, each of which may result in a different process of elimination. This process is often mixed with nonlinear and linear processes. It's important to understand that a single drug can be metabolized into different metabolites through parallel processes.
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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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The concept of therapeutic equivalence (TE) in drugs with multiple indications is complex. A generic drug may be therapeutically equivalent to a brand-name product for one specific indication, but this doesn't necessarily mean it's equivalent for all other indications. Evidence of TE in one patient group and bioequivalence shown in healthy volunteers can support—but not confirm—TE for other indications. However, definitive proof requires individual clinical studies for each...
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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Multiple Kernel Learning for Drug Discovery.

Nicholas C V Pilkington1, Matthew W B Trotter2,3, Sean B Holden4

  • 1University of Cambridge Computer Laboratory, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK phone: +44 (0)1223 763725.

Molecular Informatics
|August 2, 2016
PubMed
Summary
This summary is machine-generated.

Multiple kernel learning (MKL) enhances support vector machine (SVM) performance in chemometric analysis. This advanced method improves structure-property relationship predictions and reveals descriptor influences.

Keywords:
ChemoinformaticsDrug discoveryKernel methodsMachine learningStructure-property relationships

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Area of Science:

  • Chemometrics
  • Machine Learning
  • Computational Chemistry

Background:

  • Support Vector Machines (SVM) are widely used in chemometric analysis.
  • Multiple Kernel Learning (MKL) is a recent development in SVM algorithms.
  • Structure-Property Relationship (SPR) studies are crucial for chemical research.

Purpose of the Study:

  • To evaluate the effectiveness of Multiple Kernel Learning (MKL) on published Structure-Property Relationship (SPR) data.
  • To compare MKL performance against traditional single-kernel SVM.
  • To explore MKL's capability in identifying the influence of different structural descriptor groups.

Main Methods:

  • Application of Multiple Kernel Learning (MKL) algorithm to SPR datasets.
  • Supervised classifier creation using MKL with weighted kernels.
  • Comparison of MKL performance with single-kernel Support Vector Machine (SVM).

Main Results:

  • Statistically significant performance improvement of MKL over single-kernel SVM was observed across all three SPR datasets.
  • MKL successfully learned weights across multiple kernel representations.
  • MKL output provided insights into the relative importance of distinct descriptor subsets.

Conclusions:

  • Multiple Kernel Learning (MKL) offers a statistically significant performance enhancement for Support Vector Machine (SVM) based chemometric analysis.
  • MKL is effective for Structure-Property Relationship (SPR) studies, improving predictive accuracy.
  • MKL provides valuable information on the influence of various structural descriptors in SPR modeling.