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Related Concept Videos

Drug toxicity: Drug–Drug Interaction01:30

Drug toxicity: Drug–Drug Interaction

Drug–drug interactions can precipitate toxicity through multiple mechanisms. Absorption interactions alter how drugs enter the body, exemplified when ranitidine increases the absorption of basic drugs, while cholestyramine decreases the levels of propranolol. Protein binding interactions occur when drugs share the same binding sites on plasma proteins. Drugs like aspirin and warfarin, when bound in excess, can lead to increased free drug concentrations, enhancing the potential for...
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...
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Drug Discovery: Overview

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...
Pharmacokinetics: Drug–Drug Interactions01:25

Pharmacokinetics: Drug–Drug Interactions

Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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...
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Structure-Activity Relationships and Drug Design

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Related Experiment Video

Updated: May 9, 2026

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Similarity-based machine learning methods for predicting drug-target interactions: a brief review.

Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka

    Briefings in Bioinformatics
    |August 13, 2013
    PubMed
    Summary

    This review summarizes similarity-based machine learning methods for predicting drug-target interactions. These approaches leverage chemical and genomic spaces to identify promising drug candidates for further study.

    Keywords:
    drug discoverydrug similaritydrug–target interaction predictionmachine learningtarget similarity

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    Last Updated: May 9, 2026

    Protein Target Prediction and Validation of Small Molecule Compound
    10:21

    Protein Target Prediction and Validation of Small Molecule Compound

    Published on: February 23, 2024

    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Pharmacogenomics

    Background:

    • Predicting drug-target interactions computationally aids in selecting candidates for biochemical verification.
    • Similarity-based machine learning methods utilize drug and target similarities, reflecting chemical and genomic spaces, respectively.
    • Understanding relationships between chemical and genomic spaces is crucial in pharmacogenomics and chemical biology.

    Purpose of the Study:

    • To review state-of-the-art similarity-based machine learning methods for predicting drug-target interactions.
    • To provide an empirical comparison of these methods under a uniform experimental setting.
    • To explore the advantages and limitations of various similarity-based approaches.

    Main Methods:

    • Focus on machine learning-based approaches, specifically similarity-based methods.
    • Utilize drug similarities (chemical space) and target similarities (genomic space).
    • Combine these similarities to build predictive models for drug-target interactions.

    Main Results:

    • Similarity-based methods are promising for drug-target interaction prediction.
    • These methods are of great interest in the bioinformatics community.
    • Empirical comparison reveals the strengths and weaknesses of different techniques.

    Conclusions:

    • Similarity-based machine learning offers a powerful framework for predicting drug-target interactions.
    • Further investigation into the advantages and limitations of these methods is warranted.
    • This review provides a valuable resource for researchers in bioinformatics and drug discovery.