首页|Dalian University of Technology Reports Findings in Bacterial Infections and Mycoses (Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods)
Dalian University of Technology Reports Findings in Bacterial Infections and Mycoses (Investigation of bacterial DNA gyrase Inhibitor classification models and structural requirements utilizing multiple machine learning methods)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Bacterial Infections and Mycoses is the subject of a report. According to news originating from Liaoning, People's Republic of China, by NewsRx correspondents, research stated, "Infections from multidrug-resistant (MDR) bacteria have emerged as a paramount global health concern, and the therapeutic effectiveness of current treatments is swiftly diminishing. An urgent need exists to explore innovative strategies for countering drug-resistant bacteria." Our news journalists obtained a quote from the research from the Dalian University of Technology, "Bacterial DNA gyrase, functioning as an ATP-dependent enzyme, plays a pivotal role in the intricate processes of transcription, replication, and chromosome segregation within bacterial DNA. This renders it a prime target for the development of innovative antibacterial agents. However, the experimental identification of bacterial DNA gyrase inhibitors faces multifaceted challenges due to current methodological constraints. Recognizing its significance, this study developed 56 computational models designed for predicting bacterial DNA gyrase inhibitors. These models employed seven distinct molecular fingerprints and eight machine learning algorithms. Among these models, Model_2D, created using KlekotaRoth fingerprints and the SVM algorithm, stands out as the most robust performer (ACC = 0.86, MCC = 0.63, G-mean = 0.82). Moreover, given the limited exploration of structural fragments required for DNA Gyrase B inhibitors, crucial structural fingerprints influencing DNA Gyrase B inhibitors were identified through Bayesian classification. Subsequently, we conducted molecular docking to reveal the binding modes between these crucial structural fingerprints and the active site of DNA gyrase B."
LiaoningPeople's Republic of ChinaAsiaBacterial DNABacterial DNA TopoisomerasesBacterial Infections and MycosesCyborgsDNA GyraseDNA ResearchDrugs and TherapiesEmerging TechnologiesEnzymes and CoenzymesGeneticsGyraseHealth and MedicineMachine Learning
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