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.

中文翻译：

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2型糖尿病计算机辅助药物设计的进展

摘要

介绍

糖尿病患者的数量不断增加，给全世界带来了沉重的社会和经济负担。传统的药物开发技术费时费力，而计算机辅助药物设计（CADD）的出现改变了这一局面。本研究回顾了 CADD 在糖尿病药物设计中的应用。

涵盖的领域

在本文中，作者通过详细阐述这一发现，重点关注 CADD 在糖尿病药物设计中的进展，包括过氧化物酶体增殖物激活受体 (PPAR)、G 蛋白偶联受体 40 (GPR40)、二肽基肽酶-IV (DDP-IV) 、蛋白酪氨酸磷酸酶 1B (PTP1B)、钠依赖性葡萄糖转运蛋白 2 (SGLT-2) 和葡萄糖激酶 (GK)。这些靶点的一些药物发现与 CADD 策略有关。

专家意见

毫无疑问，CADD 有助于发现新型抗糖尿病药物。然而，仍然存在许多限制和挑战，例如缺乏共晶复合物、动态模拟、水和金属离子处理。在不久的将来，人工智能 (AI) 可能是一种很有前途的策略，可以通过识别候选药物来加速药物发现和降低成本。此外，预测蛋白质 3D 结构的深度学习模型 AlphaFold 代表了蛋白质结构预测的相当大的进步，特别是在缺乏蛋白质结构同源模板的情况下。