机器学习和深度学习方法用于增强预测hERG封锁:一个全面的QSAR建模研究
Jie Liu1, Md Kamrul Hasan Khan1, Wenjing Guo1
1National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA.
新的定量结构-活性关系 (QSAR) 模型准确预测药物诱导的hERG通道阻塞,这是心脏毒性和药物开发安全性的关键因素.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 心血管药理学心血管药理学
背景情况:
- 药物诱导的心脏毒性是药物戒断的一个重要原因.
- 该hERG通道对于心脏和神经系统的功能至关重要;其阻塞是一个主要的安全问题.
- 准确预测hERG通道阻塞对于药物安全性评估至关重要.
研究的目的:
- 开发和评估用于预测hERG通道封锁的定量结构-活动关系 (QSAR) 模型.
- 为了提高超越现有模型的hERG封锁预测的准确性.
主要方法:
- 使用大型培训数据集开发六个单独的QSAR模型和三个整体模型.
- 通过十倍交叉验证对所有模型的评估.
- 使用两个独立的外部数据集验证模型性能.
主要成果:
- 10倍交叉验证实现了马修斯相关系数 (MCC) 值从0.682到0.730,超过了之前报告的最佳模型.
- 第一个数据集的外部验证产生了从0.520到0.715的MCC值,超过了之前的模型.
- 对第二个数据集的外部验证显示MCC值在0.025和0.215之间,与现有模型一致.
结论:
- 开发的QSAR模型有效地预测了hERG通道封锁活动.
- 这些模型可以帮助制药行业和监管机构加强药物安全评估.
- 改善hERG阻塞的预测可以降低新药候选药物对心脏不良事件的风险.
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