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首页> 外文期刊>Journal of drug targeting >Physicochemical property profile for brain permeability: comparative study by different approaches
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Physicochemical property profile for brain permeability: comparative study by different approaches

机译：脑渗透性的物理化学性质谱：不同方法的比较研究

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A comparative study of classification models of brain penetration by different approaches was carried out on a training set of 1000 chemicals and drugs, and an external test set of 100 drugs. Ten approaches were applied in this work: seven medicinal chemistry approaches (including "rule of 5'' and multiparameter optimization) and also three SAR techniques: logistic regression (LR), random forest (RF) and support vector machine (SVM). Forty-one different medicinal chemistry descriptors representing diverse physicochemical properties were used in this work. Medicinal chemistry approaches based on the intuitive estimation of preference zones of CNS or non-CNS chemicals, with different rules and scoring functions, yield unbalanced models with poor classification accuracy. RF and SVM methods yielded 82% and 84% classification accuracy respectively for the external test set. LR was also successful in CNS/non-CNS (denoted in this study as CNS+/CNS-) classification and yielded an overall accuracy equivalent to that of SVM and RF. At the same time, LR is especially valuable for medicinal chemists because of its simplicity and the possibility of clear mechanistic interpretation.
机译：通过不同方法进行大脑渗透分类模型的比较研究，对1000美元的化学品和药物进行培训，以及100种药物的外部测试组。在这项工作中应用了十种方法：七种药用化学方法（包括5''和5''和Multiparameter Optimization）以及三种SAR技术：Logistic回归（LR），随机森林（RF）和支持向量机（SVM）。四十 - 在这项工作中使用了代表不同的物理化学性质的不同的药用化学描述符。药物化学方法基于CNS或非CNS化学品的偏好区直观估计，具有不同的规则和评分功能，产生不平衡模型，分类精度差。射频和SVM方法分别产生82％和84％的分类准确性。LR也在CNS /非CNS（本研究中表示为CNS + / CNS-）分类，并产生了相当于的整体准确性SVM和RF。与此同时，由于其简单性和清除机械解释的可能性，LR对药用化学家特别有价值。

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