首页|Studies from Jiangnan University Have Provided New Data on Machine Learning (Mac hine Learning-based Virtual Screening of Multitarget Anti-obesity Compounds Fro m Medicinal and Edible Plants: a Combined In Silico and In vitro Study)
Studies from Jiangnan University Have Provided New Data on Machine Learning (Mac hine Learning-based Virtual Screening of Multitarget Anti-obesity Compounds Fro m Medicinal and Edible Plants: a Combined In Silico and In vitro Study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news originating from Jiangsu, People's Republic of China, by NewsRx correspondents, research stated, "In response to the limited e ffectiveness of existing weight loss food products, we sought to apply machine l earning-based virtual screening methods to identify potential anti-obesity funct ional compounds from medicinal and edible plants and validate their in vitro act ivities. Firstly, we construct and evaluate the machine learning (ML) screening models using Multilayer Perceptron (MLP) and Random Forest (RF) algorithms." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Jiangnan University , "The receiver operating characteristic (ROC) curve demonstrates the high accur acy of MLP and RF models in screening for obese-related targets PL (pancreatic l ipase) and AMPK (Adenosine 5 '-monophosphate activated protein kinase). Subseque ntly, the tested ML models are employed to screen the constructed database, and Gypenoside LXVI (GYP) and alisol-b-23-acetate (ALI) are identified as compounds exhibiting favorable activity against both targets. The hit compounds are tested for their impact on lipase activity and lipid accumulation. The test results sh ow that GYP and ALI have favorable inhibitory effects on pancreatic lipase (PL), with IC50 of 359.7 and 433.8 mu g/mL. Furthermore, both GYP and ALI significant ly reduced cellular lipid accumulation by 72.89% and 79.01% with the concentration increase to 40 mu g/mL. The molecular docking results ind icate that GYP and ALI can interact with several amino acid residues on the two target proteins, thereby affecting the activity of the target proteins."
JiangsuPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesEnzymes and CoenzymesLipaseMachine Learni ngJiangnan University
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