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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Classification models for CYP450 3A4 inhibitors and non-inhibitors.
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Classification models for CYP450 3A4 inhibitors and non-inhibitors.

机译：CYP450 3A4抑制剂和非抑制剂的分类模型。

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摘要

Cytochrome P450 3A4 (CYP3A4) is the predominant enzyme involved in the oxidative metabolic pathways of many drugs. The inhibition of this enzyme in many cases leads to an undesired accumulation of the administered therapeutic agent. The purpose of this study is to develop in silico model that can effectively distinguish human CYP3A4 inhibitors from non-inhibitors. Structural diversity of the drug-like compounds CYP3A4 inhibitors and non-inhibitors was obtained from Fujitsu Database and Korea Research Institute of Chemical Technology (KRICT) as training and test sets, respectively. Recursive Partitioning (RP) method was introduced for the classification of inhibitor and non-inhibitor of CYP3A4 because it is an easy and quick classification method to implement. The 2D molecular descriptors were used to classify the compounds into respective inhibitors and non-inhibitors by calculation of the physicochemical properties of CYP3A4 inhibitors such as molecular weights and fractions of 2D VSA chargeable groups. The RP tree model reached 72.33% of accuracy and exceeded this percentage for the sensitivity (75.82%) parameter. This model is further validated by the test set where both accuracy and sensitivity were 72.58% and 82.64%, respectively. The accuracy of the random forest model was increased to 73.8%. The 2D descriptors sufficiently represented the molecular features of CYP3A4 inhibitors. Our model can be used for the prediction of either CYP3A4 inhibitors or non-inhibitors in the early stages of the drug discovery process.
机译：细胞色素P450 3A4（CYP3A4）是参与许多药物氧化代谢途径的主要酶。在许多情况下，对该酶的抑制导致所施用的治疗剂的不希望的积累。这项研究的目的是开发一种计算机模型，该模型可以有效地区分人类CYP3A4抑制剂与非抑制剂。药物样化合物CYP3A4抑制剂和非抑制剂的结构多样性分别来自富士通数据库和韩国化学技术研究院（KRICT）作为训练集和测试集。 CYP3A4的抑制剂和非抑制剂的分类引入了递归划分（RP）方法，因为它是一种易于实现的快速分类方法。通过计算CYP3A4抑制剂的理化特性（例如分子量和2D VSA可充电基团的分数），使用2D分子描述符将化合物分为相应的抑制剂和非抑制剂。 RP树模型达到了72.33％的准确性，并超过了灵敏度参数（75.82％）的该百分比。该测试模型进一步验证了该模型，该模型的准确性和灵敏度分别为72.58％和82.64％。随机森林模型的准确性提高到73.8％。 2D描述符足以代表CYP3A4抑制剂的分子特征。在药物发现过程的早期阶段，我们的模型可用于预测CYP3A4抑制剂或非抑制剂。

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