与实验变量相比,深度学习模型用于预测依赖时间的CYP3A4抑制
Andrin Fluetsch1, Markus Trunzer1, Grégori Gerebtzoff1
1Novartis Biomedical Research, Novartis Campus, CH-4002 Basel, Switzerland.
Chemical research in toxicology
|March 19, 2024
概括
机器学习模型通过评估CYP3A4.4的时间依赖抑制 (TDI) 来预测药物相互作用 (DDI). 这些模型的精度与体外试验试验相当,有助于早期药物设计和风险评估.
科学领域:
- 药理学和药物新陈代谢
- 计算化学的计算化学
- 机器学习在药物发现中的作用
背景情况:
- 细胞染色体P450 (CYP450) 酶的药物代谢至关重要,但可能导致药物相互作用 (DDI).
- 对CYP3A4的时间依赖性抑制 (TDI) 是临床相关DDI的一个重要原因.
- 对于CYP抑制的现有的in silico模型经常与不平衡的数据作斗争,并且很少考虑试验变化.
研究的目的:
- 开发和评估用于预测CYP3A4 TDI的机器学习模型.
- 为了改善决策,将in silico预测与实验测试变异性进行比较.
- 为了能够在药物发现过程中更早,更大规模地评估TDI潜力.
主要方法:
- 机器学习模型的开发,包括多任务学习和图形神经网络,用于TDI预测.
- 对模型的前性评估和与实验测定变异性进行比较.
- 利用公共,内部和联合学习数据集的多任务学习.
- 执行数值和三类 (负数,弱数,正数) 预测不活化率.
主要成果:
- 最终的多任务图形神经网络模型实现了较低的错误分类率:积极的TDI为8%,负的TDI为7%.
- 深度学习预测的准确性与体外实验的可重现性相当.
- 该模型成功注释了大约16000个公开可用的结构.
结论:
- 机器学习模型,特别是深度学习方法,可以准确预测CYP3A4 TDI.
- 这些模型为早期药物设计,DDI风险减轻和实验选择提供了显著的优势.
- 替代数据集的共享旨在推进公共CYP抑制建模工作.
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