Title:State-of-the-art Application of Artificial Intelligence to Transporter-centered
Functional and Pharmaceutical Research
Volume: 24
Issue: 3
Author(s): Jiayi Yin, Nanxin You, Fengcheng Li, Mingkun Lu, Su Zeng*Feng Zhu*
Affiliation:
- Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Zhejiang Province Key Laboratory of Anti-
Cancer Drug Research, Cancer Center of Zhejiang University, Hangzhou, 310058, China
- Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, The Second Affiliated Hospital,
Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence
in Medicine, Zhejiang University, Hangzhou, 310018, China
- Alibaba-Zhejiang University Joint Research Center of Future
Digital Healthcare, Hangzhou, 330110, China
Keywords:
Transporter, artificial intelligence, machine learning, deep learning, functional annotation, structure, drug-transporter interaction.
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.
