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Related Concept Videos

Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

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...
Factors Affecting Protein-Drug Binding: Drug Interactions01:23

Factors Affecting Protein-Drug Binding: Drug Interactions

Drug interactions are a critical aspect of pharmacology and can occur when two or more drugs compete for the same binding site. This competition can result in one drug displacing another, altering the effect of the displaced drug. Drug interactions are complex processes that rely heavily on how much of the displacer drug is present and how strongly it can bind to the same sites as the displaced drug.
Displacement interactions can have varying outcomes, ranging from toxicity to virtually...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

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...
Factors Affecting Protein-Drug Binding: Drug-Related Factors01:18

Factors Affecting Protein-Drug Binding: Drug-Related Factors

Drug binding to proteins is a complex phenomenon influenced by various drug-related factors, each playing a significant role in the interaction between drugs and proteins within the body.
One crucial factor in drug-protein binding is the drug's lipophilicity or its affinity for fat. More lipophilic drugs tend to have higher binding extents. For example, highly lipophilic drugs like cloxacillin exhibit substantial protein binding, with as much as 95% of the drug binding to proteins. In contrast,...
Factors Affecting Protein-Drug Binding: Protein-Related Factors01:20

Factors Affecting Protein-Drug Binding: Protein-Related Factors

Drug binding to proteins is a key aspect of pharmacokinetics and can influence a drug's distribution, absorption, and elimination in the body. Several factors, including the drug's physiochemical properties, protein concentration, disease states, and the number of binding sites on the protein, influence this process.
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Related Experiment Video

Updated: May 21, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization.

Mehmet Gönen1

  • 1Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Espoo, Finland. mehmet.gonen@aalto.fi

Bioinformatics (Oxford, England)
|June 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian method to predict drug-target interactions using chemical and genomic similarities. The approach effectively forecasts interactions for new drugs and identifies unknown connections, advancing drug discovery.

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Last Updated: May 21, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug discovery

Background:

  • Accurate drug-target interaction identification is crucial for drug discovery.
  • Current databases lack sufficient experimentally validated interactions.
  • Predicting these interactions computationally remains a significant challenge.

Purpose of the Study:

  • To develop a novel computational method for predicting drug-target interactions.
  • To leverage chemical and genomic similarities for enhanced prediction accuracy.
  • To address the limitations of existing drug-target interaction databases.

Main Methods:

  • A novel Bayesian formulation integrating dimensionality reduction, matrix factorization, and binary classification.
  • Jointly projecting drug compounds and target proteins into a unified subspace.
  • Utilizing variational approximation for efficient inference.

Main Results:

  • The method accurately predicts drug-target interactions across four human networks (enzymes, ion channels, GPCRs, nuclear receptors).
  • Demonstrated effectiveness in exploratory data analysis with low-dimensional projections.
  • Successfully predicted out-of-sample drug interactions and identified unknown interactions within existing networks.

Conclusions:

  • The proposed Bayesian approach offers a powerful tool for predicting drug-target interactions.
  • This method enhances drug discovery by improving the identification of potential drug-target relationships.
  • The framework provides a unified subspace for analyzing and predicting interactions.