Maschinelles Lernen zur Vorhersage antibakterieller Aktivität von Ruthenium‐Komplexen**
Markus Orsi,
Boon Shing Loh,
Cheng Weng
et al.
Abstract:Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low‐data scenarios. For the first time, we extend the a… Show more
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