利用分层化学图表示和多分辨率图变分自编码器的基于片段的深度分子生成。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Zhenxiang Gao, Xinyu Wang, Blake Blumenfeld Gaines, Xuetao Shi, Jinbo Bi, Minghu Song
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引用次数: 0

摘要

图生成模型是最近出现的一种有趣的方法,可以逐个原子或逐个片段地构建分子结构。在本研究中,我们采用基于片段的策略,将每个输入分子分解为一组小的化学片段。在药物发现中,一些药物分子是通过用它们的生物同工异构体或替代化学部分取代某些化学取代基来设计的。这启发我们将分解后的碎片根据断键位置周围的局部结构环境分成不同的碎片簇。通过这种方式,输入结构可以转换为等效的三层图,其中单个原子、分解的片段或获得的片段簇作为每个相应层的图节点。我们进一步实现了一个名为多分辨率图变分自编码器(MRGVAE)的原型模型,以从细到粗的顺序学习每层构成节点的嵌入。我们的解码器采用类似但相反的分层结构。它首先预测下一个可能的片段簇,然后从确定的片段簇中采样一个精确的片段结构,并按顺序将其连接到前面的化学片段。与其他几种基于图的分子生成模型相比,我们提出的方法在分子评价指标方面表现出相对较好的性能。引入额外的片段聚类图层有望增加组装原始训练集中缺失的新化学片段的几率,并增强其结构多样性。我们希望我们的原型工作将激发更多的创造性研究,以探索将不同种类的化学领域知识纳入类似的多分辨率神经网络架构的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder.

Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder.

Graph generative models have recently emerged as an interesting approach to construct molecular structures atom-by-atom or fragment-by-fragment. In this study, we adopt the fragment-based strategy and decompose each input molecule into a set of small chemical fragments. In drug discovery, a few drug molecules are designed by replacing certain chemical substituents with their bioisosteres or alternative chemical moieties. This inspires us to group decomposed fragments into different fragment clusters according to their local structural environment around bond-breaking positions. In this way, an input structure can be transformed into an equivalent three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer. We further implement a prototype model, named multi-resolution graph variational autoencoder (MRGVAE), to learn embeddings of constituted nodes at each layer in a fine-to-coarse order. Our decoder adopts a similar but conversely hierarchical structure. It first predicts the next possible fragment cluster, then samples an exact fragment structure out of the determined fragment cluster, and sequentially attaches it to the preceding chemical moiety. Our proposed approach demonstrates comparatively good performance in molecular evaluation metrics compared with several other graph-based molecular generative models. The introduction of the additional fragment cluster graph layer will hopefully increase the odds of assembling new chemical moieties absent in the original training set and enhance their structural diversity. We hope that our prototyping work will inspire more creative research to explore the possibility of incorporating different kinds of chemical domain knowledge into a similar multi-resolution neural network architecture.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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