摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据来自英国巴斯的新闻，NewsRx记者报道，研究表明：“本研究评估了Machine Learning利用药物吸收、分布、代谢和排泄(ADME)数据以及理化和药理学数据来开发一种新的抗胆碱能负荷量表，并将其性能与以前的PU Blled量表进行了比较。在实验和电子ADME中，收集了抗毒蕈碱活性的理化和药理学数据。"血脑屏障的形成,生物利用度,化学结构和p-糖蛋白(p-gp)底物."我们的新闻记者引用了Bath大学的一篇研究，“利用这五种药物的性质训练一个无监督模型来计算药物的抗胆碱能负荷评分，通过10倍交叉验证来评价模型的性能，并与临床抗胆碱能认知负荷(ACB)量表和非临床抗胆碱能毒性评分表(ATS)进行比较。”在用于筛选血脑屏障(BBB)穿透的药物的Silico Software(ADMET Predictor)中，正确地识别了一些不穿过血脑屏障的药物。基于五个选择变量的无监督和ACB量表曲线下的平均面积分别为0.76和0.64，无监督模型同意ACB量表对一半以上的药物进行分类(88个中的49个)，同意ATS量表对不到一半的药物进行分类(25个中的12个)。我们的发现表明，常用的ACB量表可能会因为某些药物无法跨越BBB而错误分类。"ATS SCAL E会仅仅根据毒蕈碱结合亲和力而不考虑其他药物性质而错误地分类药物."
Abstract
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 originating from Bath, United Kingdom, by NewsRx correspondents, research stated, "This study evaluated the use of mach ine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously pu blished scales. Experimental and in silico ADME, physicochemical and pharmacolog ical data were collected for antimuscarinic activity, blood-brain barrier penetr ation, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate p rofile." Our news journalists obtained a quote from the research from the University of B ath, "These five drug properties were used to train an unsupervised model to ass ign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (AT S) scale, which is based primarily on muscarinic binding affinity. In silico sof tware (ADMET Predictor) used for screening drugs for their blood-brain barrier ( BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five s elected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (1 2 of 25). Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scal e would misclassify drugs solely depending on muscarinic binding affinity withou t considering other drug properties."
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