syntalink - hybrid:一种针对特定靶标药物设计的深度学习方法

Yu Feng , Yuyao Yang , Wenbin Deng , Hongming Chen , Ting Ran
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引用次数: 0

摘要

靶向性药物设计在药物发现领域受到广泛关注。但是,如何有效地探索靶向化学领域是一个巨大的挑战。基于片段的药物设计(FBDD)已经显示出它在这方面的潜力。在这项研究中,我们引入了一种基于深度学习的片段连接方法,即SyntaLinker-Hybrid,用于目标特定分子的生成。该方法通过迁移学习和片段杂交,可以产生大量的连接子片段,将给定的末端片段组装成具有目标特异性的分子。这项工作表明,该方法具有为各种目标生成目标特定结构的能力。我们认为,它的适用可以扩大到更广泛的目标范围。
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SyntaLinker-Hybrid: A deep learning approach for target specific drug design

Target specific drug design has attracted much attention in drug discovery. But, it is a great challenge to efficiently explore the target-focused chemical space. Fragment-based drug design (FBDD) has shown its potential to do this thing. In this study, we introduced a deep learning-based fragment linking method, namely SyntaLinker-Hybrid, for target specific molecular generation. By carrying out transfer learning and fragment hybridization, this method allows to generate a great number of linker fragments to assemble given terminal fragments into the molecules with target specificity. This work demonstrates that the method has the capacity to generate target specific structures for various targets. We believe that its application could be extended to a broader target scope.

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来源期刊
Artificial intelligence in the life sciences
Artificial intelligence in the life sciences Pharmacology, Biochemistry, Genetics and Molecular Biology (General), Computer Science Applications, Health Informatics, Drug Discovery, Veterinary Science and Veterinary Medicine (General)
CiteScore
5.00
自引率
0.00%
发文量
0
审稿时长
15 days
期刊最新文献
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