首页|Qilu University of Technology (Shandong Academy of Sciences) Reports Findings in Machine Learning (Prediction of quality markers in Maren Runchang pill for cons tipation using machine learning and network pharmacology)
Qilu University of Technology (Shandong Academy of Sciences) Reports Findings in Machine Learning (Prediction of quality markers in Maren Runchang pill for cons tipation using machine learning and network pharmacology)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Jinan, People's Republic of China, by NewsRx journalists, research stated, "Maren Runchang pill (MRRCP) is a Chinese patent medicine used to treat constipation in clinics. It has multi -component and multi-target characteristics, and there is an urgent need to scre en markers to ensure its quality." Funders for this research include Key Technology Research and Development Progra m of Shandong Province, Qilu University of Technology, Agriculture Research Syst em of China. The news correspondents obtained a quote from the research from the Qilu Univers ity of Technology (Shandong Academy of Sciences), "The aim of this study was to screen quality markers of MRRCP based on a ‘differential compounds-bioactivity' strategy using machine learning and network pharmacology to ensure the effective ness and stability of MRRCP. In this study, UPLC-Q-TOF-MS/MS was used to identif y chemical compounds in MRRCP and machine learning algorithms were applied to sc reen differential compounds. The quality markers were further screened by networ k pharmacology. Meanwhile, molecular docking was used to verify the screening re sults of machine learning and network pharmacology. A total of 28 constituents i n MRRCP were identified, and four differential compounds were screened by machin e learning algorithms. Subsequently, a total of two quality markers (rutin and r ubiadin) in MRRCP. Additionally, the molecular docking results showed that quali ty markers could spontaneously bind to core targets. This study provides a refer ence for improving the quality evaluation method of MRRCP to ensure its quality. "
JinanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesHealth and MedicineMachine LearningPharmace uticalsPharmacology
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