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Related Concept Videos

Protein Networks02:26

Protein Networks

4.3K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Related Experiment Video

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations.

Qichang Zhao, Mengyun Yang, Zhongjian Cheng

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |March 26, 2021
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    Summary
    This summary is machine-generated.

    Deep learning significantly enhances compound-protein relation (CPR) prediction for drug discovery. This study reviews deep learning methods, datasets, and future directions for predicting compound-protein interactions and affinities.

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    Area of Science:

    • Computational chemistry and cheminformatics
    • Bioinformatics and computational biology
    • Machine learning in drug discovery

    Background:

    • Identifying compound-protein relations (CPRs), including interactions (CPIs) and affinities (CPAs), is crucial for drug development.
    • Traditional in vitro screening methods are costly, time-consuming, and have high failure rates due to the vast number of compounds and proteins.
    • Virtual screening (VS) computational methods leverage biological interaction data and molecular properties to predict CPRs.

    Purpose of the Study:

    • To investigate and discuss the latest applications of deep learning techniques in compound-protein relation prediction.
    • To provide researchers with insights into current deep learning methods for CPR prediction and future research directions.

    Main Methods:

    • Description of commonly used datasets and feature engineering techniques (compound and protein representations/descriptors) for CPR prediction.
    • Review and classification of recent deep learning approaches applied to CPR prediction.
    • Comprehensive comparison of the prediction performance of representative deep learning methods on benchmark datasets.

    Main Results:

    • Deep learning methods have demonstrated rapid development and achieved significant results in predicting compound-protein relations.
    • Representative deep learning approaches show promising prediction performance on classical CPR datasets.
    • Analysis highlights the effectiveness of deep learning in overcoming limitations of traditional experimental methods.

    Conclusions:

    • Deep learning is a powerful tool for enhancing CPR predictions, accelerating the drug discovery process.
    • The study identifies current challenges and proposes future directions for developing advanced deep learning models in this field.
    • This review serves as a valuable reference for researchers aiming to improve CPR prediction accuracy and efficiency.