deepFPlearn +:使用图形神经网络在整个化学宇宙中增强毒性预测
Kyriakos Soulios1,2, Patrick Scheibe3, Matthias Bernt1
1Department of Computation Biology, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany.
Bioinformatics (Oxford, England)
|November 27, 2023
概括
我们用图形神经网络 (GNN) 和先进的分割策略来增强deepFPlearn,以改进在中毒性预测. 这个新版本,deepFPlearn+,为化学风险评估提供了卓越的概括.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 人工智能的人工智能是人工智能.
背景情况:
- 越来越多的化学物质需要有效的in silico方法来预测毒性,以支持风险评估.
- 像deepFPlearn这样的现有方法为计算毒理学提供了基础.
- 化学开发方面的创新超过了监管能力,突出了先进的预测工具的需要.
研究的目的:
- 通过增强的分子表示和强大的验证策略来扩展deepFPlearn应用程序.
- 提高in silico毒性预测模型的准确性和通用性.
- 为实验性测试提供可靠的化学品优先级工具.
主要方法:
- 纳入图形神经网络 (GNN) 进行复杂的分子结构表示.
- 基于脚手架结构和分子重量的替代火车测试分割策略的实施.
- 在具有挑战性的,未见的数据集上验证增强模型的性能.
主要成果:
- 图形神经网络显著优于之前的深度FPlearn模型.
- deepFPlearn+在强大而具有挑战性的测试集上展示了卓越的概括能力.
- 改进的模型有效地预测化学毒性,准确度提高.
结论:
- deepFPlearn+在in silico毒性预测方面取得了重大进展.
- 强烈建议将该工具应用于化学库存,以评估风险和确定优先级.
- 改进后的模型有助于有效的化学安全评估和监管支持.
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