机器学习和大型语言模型用于模拟复杂的毒性途径和预测类固醇生成
Thomas R Lane1, Patricia A Vignaux1, Joshua S Harris1
1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States of America.
Environmental science & technology
|June 27, 2025
概括
我们开发了计算模型来预测化学物质如何影响类固醇生成,即激素生产过程. 这些模型为评估化学物质影响提供了一个快速系统,有助于制定监管决策.
科学领域:
- 内分泌学 在内分泌学.
- 计算毒理学计算毒理学
- 药理学 药理学是指药理学的学科.
背景情况:
- 雌激素和雄激素受体相互作用的模型很好,但类固醇生成的预测仍然有限.
- 类固醇生成对激素调节至关重要,是化学破坏的目标.
- 目前用于评估化学物质对类固醇生成的影响的方法不足以进行大规模查.
研究的目的:
- 开发和验证用于预测类固醇生成的化学调制的计算模型.
- 在受化学物质影响的类固醇生成途径中识别特定的分子标.
- 为化学风险评估和监管评估提供一个可扩展的系统.
主要方法:
- 利用在H295R细胞中选的约1800种化学物质的数据来构建随机森林模型.
- 开发了使用IC50数据从ChEMBL获得关键类固醇酶的分类和回归模型.
- 采用基于变压器的模型 (MolBART) 来同时预测多个终点.
主要成果:
- 随机森林模型在一般类固醇生成调节的前性验证中实现了80%的准确性.
- 开发了包括CYP17A1,CYP21A2和CYP19A1在内的关键酶的模型.
- 变压器模型证明了用于同时预测所有终点的验证性能.
结论:
- 开发的模型提供了一种快速可扩展的方法来评估化学物质对类固醇生成的影响.
- 这些工具可以支持化学风险评估,产品管理和监管决策.
- 这些模型可以预测一般的类固醇生成抑制和特定的酶标.
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