{"title":"Deep learning as a highly efficient tool for digital signal processing design","authors":"Andrey Pryamikov","doi":"10.1038/s41377-024-01599-8","DOIUrl":null,"url":null,"abstract":"<p>The backpropagation algorithm, the most widely used algorithm for training artificial neural networks, can be effectively applied to the development of digital signal processing schemes in the optical fiber transmission systems. Digital signal processing as a deep learning framework can lead to a new highly efficient paradigm for cost-effective digital signal processing designes with low complexity.</p>","PeriodicalId":20,"journal":{"name":"ACS Medicinal Chemistry Letters","volume":"40 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Medicinal Chemistry Letters","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1038/s41377-024-01599-8","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0
Abstract
The backpropagation algorithm, the most widely used algorithm for training artificial neural networks, can be effectively applied to the development of digital signal processing schemes in the optical fiber transmission systems. Digital signal processing as a deep learning framework can lead to a new highly efficient paradigm for cost-effective digital signal processing designes with low complexity.
期刊介绍:
ACS Medicinal Chemistry Letters is interested in receiving manuscripts that discuss various aspects of medicinal chemistry. The journal will publish studies that pertain to a broad range of subject matter, including compound design and optimization, biological evaluation, drug delivery, imaging agents, and pharmacology of both small and large bioactive molecules. Specific areas include but are not limited to:
Identification, synthesis, and optimization of lead biologically active molecules and drugs (small molecules and biologics)
Biological characterization of new molecular entities in the context of drug discovery
Computational, cheminformatics, and structural studies for the identification or SAR analysis of bioactive molecules, ligands and their targets, etc.
Novel and improved methodologies, including radiation biochemistry, with broad application to medicinal chemistry
Discovery technologies for biologically active molecules from both synthetic and natural (plant and other) sources
Pharmacokinetic/pharmacodynamic studies that address mechanisms underlying drug disposition and response
Pharmacogenetic and pharmacogenomic studies used to enhance drug design and the translation of medicinal chemistry into the clinic
Mechanistic drug metabolism and regulation of metabolic enzyme gene expression
Chemistry patents relevant to the medicinal chemistry field.