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Related Concept Videos

The Significance of Membrane Transport01:44

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The transport of solutes across the cell membrane is essential for metabolic processes, like maintaining cell size and volume, generating the action potential, exchanging nutrients and gases, etc. Membrane transport can be either passive or active. It can be simple diffusion, facilitated, or mediated transport aided by transport proteins such as transporters and channels.
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Carrier-mediated transport is a pivotal process in drug absorption, particularly for lipid-insoluble drugs, and encompasses facilitated diffusion and active transport. Facilitated diffusion allows drugs to move along their concentration gradient without energy expenditure, while active transport utilizes ATP to drive drug movement against this gradient.
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Membrane Transporters01:31

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Transporters are essential membrane transport proteins with functions related to cell nutrition, homeostasis, communication, etc. Approximately 7% of all genes in the human genome code for transporters or transporter-related proteins.
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In contrast to passive transport, active transport involves a substance being moved through membranes in a direction against its concentration or electrochemical gradient. There are two types of active transport: primary active transport and secondary active transport. Primary active transport utilizes chemical energy from ATP to drive protein pumps embedded in the cell membrane. With energy from ATP, the pumps transport ions against their electrochemical gradients—a direction they would...
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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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Related Experiment Video

Updated: Jul 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Transporter proteins knowledge graph construction and its application in drug development.

Xiao-Hui Chen1, Yao Ruan1, Yan-Guang Liu1

  • 1Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China.

Computational and Structural Biotechnology Journal
|May 26, 2023
PubMed
Summary

This study introduces a transporter-related knowledge graph (KG) and AI models to aid drug discovery. These tools improve the prediction and design of transporter-related drugs, enhancing pharmacokinetic properties.

Keywords:
Generative modelKG embeddingPredictive modelTransporter knowledge graph

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Bioinformatics

Background:

  • Drug transporters significantly influence drug pharmacokinetics (absorption, distribution, excretion).
  • Experimental validation and structural analysis of membrane transporters are challenging.
  • Knowledge graphs (KGs) can effectively uncover complex associations between biological entities.

Purpose of the Study:

  • To construct a transporter-related knowledge graph (KG) to enhance drug discovery.
  • To develop predictive (AutoInt_KG) and generative (MolGPT_KG) AI models using KG data.
  • To validate the reliability and utility of these AI models for drug design.

Main Methods:

  • Construction of a comprehensive transporter-related knowledge graph.
  • Development of the AutoInt_KG predictive framework using the RESCAL model.
  • Establishment of the MolGPT_KG generative framework for novel molecule design.
  • Validation using natural product Luteolin and molecular docking analysis.

Main Results:

  • The AutoInt_KG framework demonstrated high reliability in predicting transporter interactions (ROC-AUC: 0.91-0.94, PR-AUC: 0.78-0.91).
  • The MolGPT_KG framework successfully generated novel, valid molecules with potential transporter-binding capabilities.
  • Molecular docking confirmed that generated molecules bind to key amino acids in target transporter active sites.

Conclusions:

  • The developed transporter-related KG and AI frameworks offer valuable resources for drug discovery.
  • These computational tools can guide the design and development of novel transporter-related drugs.
  • The findings provide a foundation for improving drug pharmacokinetics through targeted transporter modulation.