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Current Drug Metabolism

Editor-in-Chief

ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Mini-Review Article

State-of-the-art Application of Artificial Intelligence to Transporter-centered Functional and Pharmaceutical Research

Author(s): Jiayi Yin, Nanxin You, Fengcheng Li, Mingkun Lu, Su Zeng* and Feng Zhu*

Volume 24, Issue 3, 2023

Published on: 08 June, 2023

Page: [162 - 174] Pages: 13

DOI: 10.2174/1389200224666230523155759

Price: $65

Abstract

Protein transporters not only have essential functions in regulating the transport of endogenous substrates and remote communication between organs and organisms, but they also play a vital role in drug absorption, distribution, and excretion and are recognized as major determinants of drug safety and efficacy. Understanding transporter function is important for drug development and clarifying disease mechanisms. However, the experimental-based functional research on transporters has been challenged and hinged by the expensive cost of time and resources. With the increasing volume of relevant omics datasets and the rapid evolution of artificial intelligence (AI) techniques, next-generation AI is becoming increasingly prevalent in the functional and pharmaceutical research of transporters. Thus, a comprehensive discussion on the state-of-the-art application of AI in three cutting-edge directions was provided in this review, which included (a) transporter classification and function annotation, (b) structure discovery of membrane transporters, and (c) drug-transporter interaction prediction. This study provides a panoramic view of AI algorithms and tools applied to the field of transporters. It is expected to guide a better understanding and utilization of AI techniques for in-depth studies of transporter-centered functional and pharmaceutical research.

Keywords: Transporter, artificial intelligence, machine learning, deep learning, functional annotation, structure, drug-transporter interaction.

Graphical Abstract
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