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Machine Learning Techniques in Antibiotic Discovery

Enderle, Tilman; Remmele, Nina; Stöcker, Jakob

Abstract (englisch):

Background: Artificial intelligence as well as machine learning are now widely used, including in medicine. One potential use case is an otherwise lengthy and yet not necessarily successful process, the discovery of new antibiotics. This process, marked by setbacks and costly, is becoming less attractive to pharmaceutical companies, resulting in fewer antibiotics being developed. In addition, there is the increased incidence of multidrug-resistant germs that cannot be controlled by conventional antibiotics, creating additional demand.
Objective: This paper addresses the question of which machine learning methods are applied in which steps of the discovery of a new antibiotic, in which steps the application of the methods makes sense, which advantages and disadvantages they entail, and an outlook on further potentials.
Method: For the literature review, a forward search was performed on three different databases, starting from a search string containing keywords relevant to the topic. The initial 850 hits could be narrowed down to 25 relevant publications after further screening.
Results: The literature found shows that neural networks, support vector machines as well as decision trees have been used so far in the generation and discovery of structures of new potential drugs, but also in the assessment of potential efficacy.
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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: 1000139014
Erschienen in cii Student Papers - 2021. Ed.: A. Sunyaev
Verlag Karlsruher Institut für Technologie (KIT)
Seiten 31-42
Schlagwörter machine learning, antibiotics, artificial neural networks, support vector machine, decision tree, random forest
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