首页|National University of Singapore Reports Findings in Antibiotics (Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes)
National University of Singapore Reports Findings in Antibiotics (Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Antibiotics is the subject of a report. Accord- ing to news reporting originating from Singapore, Singapore, by NewsRx correspondents, research stated, “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 com- pounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far.” Our news editors obtained a quote from the research from the National University of Singapore, “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 application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7 %) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4 %), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics.” According to the news editors, the research concluded: “This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.” This research has been peer-reviewed.
SingaporeSingaporeAsiaAntibacterialsAntibioticsAn- timicrobialsCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineHeavy MetalsMachine LearningRutheniumTransition Elements
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Wiley
