Advances in computer-aided drug design for type 2 diabetes

ABSTRACT

Introduction

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

Areas covered

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

Expert opinion

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

KEYWORDS:

• CADD
• type 2 diabetes
• ligand-based drug design
• structure-based drug design

Article highlights

• T2DM is a lifelong disease bring huge burden on patient and society.

• Traditional drug development technology is time-consuming and costly.

• Combining LBDD with SBDD methods holds great potential for the identification of new anti-diabetic agents.

• Future CADD study on anti-diabetic therapies should performed multiple strategies to regulate multiple pathogenesis.

• AI is a promising tool for the identification of new anti-diabetic drugs.

Declaration of Interest

The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer Disclosures

Peer reviewers in this manuscript have no relevant financial or other relationships to disclose

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (Grant 81803341), the Natural Science Foundation of Guangdong, China (Grant 2018A030313445), the Guangdong Basic and Applied Basic Research Foundation (Grant 2019A1515011036), the Key Field R&D Plan Project of Guangdong province (No. 2019B020201002), the Innovative strong school project of Guangdong Pharmaceutical University (Grant 2018KTSCX111) and the Innovation Team Projects in Universities of Guangdong Province (No. 2018KCXTD016). The authors also declare support via the projects of Guangzhou key laboratory of construction and application of new drug screening model systems (No. 201805010006) and the Key Laboratory of New Drug Discovery and Evaluation of ordinary universities of Guangdong province (No. 2017KSYS002).