通过深度学习基于患者基因表达谱的从头药物设计。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL Molecular Informatics Pub Date : 2023-08-01 Epub Date: 2023-08-21 DOI:10.1002/minf.202300064
Chikashige Yamanaka, Shunya Uki, Kazuma Kaitoh, Michio Iwata, Yoshihiro Yamanishi
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引用次数: 0

摘要

计算从头药物设计是医学中一个具有挑战性的问题,需要考虑疾病状态下生物系统的所有相关信息。在这里,我们提出了一种新的计算方法,通过深度学习从患者基因表达谱中生成候选药物分子结构,我们称之为DRAGONET。我们的模型可以产生新的分子,这些分子可能会抵消患者的疾病特异性基因表达模式,这是通过探索基于转换器的变分自动编码器构建的潜在空间并整合疾病相关分子的亚结构而实现的。我们应用DRAGONET生成癌症、特应性皮炎和阿尔茨海默病的候选药物分子,并证明新生成的分子在化学上与每种疾病的注册药物相似。这种方法适用于治疗靶蛋白未知的疾病,将对精准医学领域做出重大贡献。
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De novo drug design based on patient gene expression profiles via deep learning.

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

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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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