Exploring Novel Fentanyl Analogues Using a Graph-Based Transformer Model

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Guangle Zhang, Yuan Zhang, Ling Li, Jiaying Zhou, Honglin Chen, Jinwen Ji, Yanru Li, Yue Cao, Zhihui Xu, Cong Pian
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Abstract

The structures of fentanyl and its analogues are easy to be modified and few types have been included in database so far, which allow criminals to avoid the supervision of relevant departments. This paper introduces a molecular graph-based transformer model, which is combined with a data augmentation method based on substructure replacement to generate novel fentanyl analogues. 140,000 molecules were generated, and after a set of screening, 36,799 potential fentanyl analogues were finally obtained. We calculated the molecular properties of 36,799 potential fentanyl analogues. The results showed that the model could learn some properties of original fentanyl molecules. We compared the generated molecules from transformer model and data augmentation method based on substructure replacement with those generated by the other two molecular generation models based on deep learning, and found that the model in this paper can generate more novel potential fentanyl analogues. Finally, the findings of the paper indicate that transformer model based on molecular graph helps us explore the structure of potential fentanyl molecules as well as understand distribution of original molecules of fentanyl.

Graphical Abstract

Abstract Image

利用基于图形的变换器模型探索新型芬太尼类似物
芬太尼及其类似物的结构容易被改造,目前纳入数据库的种类较少,这使得不法分子可以规避相关部门的监管。本文介绍了一种基于分子图的变换器模型,并结合基于子结构置换的数据扩增方法生成新型芬太尼类似物。生成了 140,000 个分子,经过一系列筛选,最终得到了 36,799 个潜在的芬太尼类似物。我们计算了 36,799 种潜在芬太尼类似物的分子性质。结果表明,该模型可以学习原始芬太尼分子的一些特性。我们将转换器模型和基于子结构替换的数据增强方法生成的分子与其他两种基于深度学习的分子生成模型生成的分子进行了比较,发现本文中的模型可以生成更多新颖的潜在芬太尼类似物。最后,本文的研究结果表明,基于分子图的变换器模型有助于我们探索潜在芬太尼分子的结构,并了解芬太尼原始分子的分布情况。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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