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Comparing Machine Learning Models for Aromatase (P450 19A1)

机译：芳香糖基机学习模型（P450 19A1）

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摘要

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 the 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 a more holistic assessment of endocrine-disrupting potential of chemicals with limited empirical data and enable the reduction of the use of hazardous substances.
机译：芳族酶或细胞色素P450 19a1，催化雌激素对体内雌激素的芳香化。该酶活性的变化可以产生对性和骨骼发育不利的激素不平衡。可以用药物和天然产品以及环境化学品，抑制这种酶。因此，通过外源化学品预测潜在的内分泌破坏需要除雄激素和雌激素途径干扰之外，还考虑芳香酶抑制。贝叶斯机器学习方法可用于来自分子结构的前瞻性预测，而无需实验数据。这里，描述了利用不同芳族酶抑制数据的多种机器学习模型的产生和评估。这些模型应用于两个测试集，用于与公共领域的药物发现相关的分子。此外，通过比较培训数据的内部五倍交叉验证统计数据来评估多机器学习算法的性能。这些方法以预测来自分子结构的芳族酶抑制，当与雌激素和雄激素机学习模型一起使用时，允许更全面的评估化学品的内分泌破坏潜力，具有有限的经验数据，使得减少有害物质的使用。

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  • 来源
    《Environmental Science & Technology》 |2020年第23期|15546-15555|共10页
  • 作者

    Kimberley M. Zorn; Daniel H. Foil; Thomas R. Lane; Wendy Hillwalker; David J. Feifarek; Frank Jones; William D. Klaren; Ashley M. Brinkman; Sean Ekins;

  • 作者单位

    Collaborations Pharmaceuticals Inc. Raleigh North Carolina 27606 United States;

    Collaborations Pharmaceuticals Inc. Raleigh North Carolina 27606 United States;

    Collaborations Pharmaceuticals Inc. Raleigh North Carolina 27606 United States;

    Global Product Safety SC Johnson and Son Inc. Racine Wisconsin 53404 United States;

    Global Product Safety SC Johnson and Son Inc. Racine Wisconsin 53404 United States;

    Global Product Safety SC Johnson and Son Inc. Racine Wisconsin 53404 United States;

    Global Product Safety SC Johnson and Son Inc. Racine Wisconsin 53404 United States;

    Global Product Safety SC Johnson and Son Inc. Racine Wisconsin 53404 United States;

    Collaborations Pharmaceuticals Inc. Raleigh North Carolina 27606 United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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