Comparing Machine Learning Models for Aromatase (P450 19A1).
Comparing Machine Learning Models for Aromatase (P450 19A1).
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DOI：
10.1021/acs.est.0c05771
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发表时间：
2020-12-01
影响因子：
11.4
通讯作者：
Ekins S
中科院分区：
文献类型：
--
作者：
Zorn KM;Foil DH;Lane TR;Hillwalker W;Feifarek DJ;Jones F;Klaren WD;Brinkman AM;Ekins S
芳香酶或细胞色素P450 19A1，将雄激素的芳香剂催化为体内的雌激素。该酶活性的变化会产生可能不利于性和骨骼发育的激素失衡。药物和天然产物以及环境化学物质可能会抑制这种酶。因此，通过外源性化学物质预测潜在的内分泌破坏需要除雄激素和雌激素途径干扰外，还要考虑芳香酶抑制。贝叶斯机器学习方法可用于从分子结构的前瞻性预测，而无需实验数据。在此描述了利用不同来源的芳香酶抑制数据来源的多个机器学习模型的生成和评估。这些模型应用于两个测试集，用于外部验证，并与公共领域的药物发现有关的分子。此外，通过比较培训数据的内部五倍交叉验证统计来评估多个机器学习算法的性能。这些方法可预测分子结构中的芳香酶抑制作用（与雌激素和雄激素机器学习模型一起使用时），可以对具有有限的经验数据的化学物质破坏潜在的内分泌破坏潜力进行更多的整体评估，并可以减少使用危险物质的使用。
Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals. Therefore, predicting potential endocrine disruption via exogenous chemicals requires that aromatase inhibition be considered in addition to androgen and estrogen pathway interference. Bayesian machine learning methods can be used for prospective prediction from molecular structure without the need for experimental data. Herein, the generation and evaluation of multiple machine learning models utilizing different sources of aromatase inhibition data are described. These models are applied to two test sets for external validation with molecules relevant to drug discovery from the public domain. In addition, the performance of multiple machine learning algorithms was evaluated by comparing internal five-fold cross-validation statistics of the training data. These methods to predict aromatase inhibition from molecular structure, when used in concert with estrogen and androgen machine learning models, allow for more holistic assessment of endocrine disrupting potential of chemicals with limited empirical data and enable the reduction of use of hazardous substances.
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影响因子：
4.6
作者：
Cevik, Ulviye Acar;Cavusoglu, Betuel Kaya;Kaplancikli, Zafer Asim
通讯作者：
Kaplancikli, Zafer Asim
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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