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Review: Machine Learning Methods in Antibiotic Discovery

Seitter, Eileen; Buttenberg, Tessa; Akgöz, Eda; Karyagdi, Emre

Abstract (englisch):
Background: The World Health Organization considers antibiotic resistance as one of the greatest economic and public health challenges of our time, which is why there is an urgent need for the discovery of new antibiotics. To improve the research process regarding the time and economic resources, machine learning techniques are used. These can be applied at various points in the development process and can be realized with different concepts. An overview of the most current and promising approaches is therefore of significant importance.
Objective: We aimed to provide an overview of the use of machine learning techniques in antibiotic discovery. The objective was to identify the most important methods that are useful and promising in antibiotic research. We sought to classify existing approaches by area of application.
Methods: The research paper was based on a systematic literature search including the databases ACM Digital Library, AIS EBSCOhost, IEEE Xplore Digital Library and PubMed. After defining central core terms and a resulting search string, 489 results were obtained. These were filtered and grouped by temporal currency (from december 2019 to June 2020) and relevance. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000138902
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Buchaufsatz
Publikationsmonat/-jahr 10.2021
Sprache Deutsch
Identifikator KITopen-ID: 1000139016
Erschienen in cii Student Papers - 2021. Ed.: A. Sunyaev
Verlag Karlsruher Institut für Technologie (KIT)
Seiten 43-58
Schlagwörter antibiotic discovery, machine learning, deep learning, neuronal network, virtual screening, quantitative-structure-activity-relationship, fragment-based drug discovery, phenotypic drug discovery, prototype-based drug discovery, drug interaction
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