不良结果路径和机器学习预测药物诱导的发作责任
Thomas R Lane1, Scott H Snyder1, Joshua S Harris1
1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
ACS chemical neuroscience
|May 14, 2025
概括
开发机器学习模型来预测药物诱导的 (DIS) 可以减少中枢神经系统 (CNS) 的药物消耗. 针对特定目标的模型准确地预测DIS风险,帮助早期毒性测试和优化药物开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 毒理学 毒理学 毒理学
- 计算化学计算化学
背景情况:
- 中枢神经系统 (CNS) 药物面临高临床消耗率,通常是由于与中枢神经系统相关的毒性,如药物诱导的 (DIS).
- 早期识别DIS风险对于优化药物开发和减少昂贵的晚期失败至关重要.
- 负面结果途径 (AOPs) 为了解毒性机制提供了一个框架.
研究的目的:
- 开发和验证用于预测药物诱导性发作 (DIS) 风险的机器学习模型.
- 利用发作相关的不良结果途径 (AOPs) 来确定与DIS相关的关键蛋白质标.
- 通过早期毒性预测,降低中枢神经系统 (CNS) 药物开发中的消耗率.
主要方法:
- 通过使用相关的不良结果途径 (AOPs) 确定了67个发作相关的蛋白质标.
- 来自ChEMBL的精选生物活动数据用于模型开发.
- 开发和评估了大约2000个回归和分类模型,包括支持向量回归 (SVR),随机森林和XGBoost.
主要成果:
- 支持矢量回归 (SVR) 模型实现了0.54 ± 0.09 (-log M) 的平均平均绝对误差 (MAE).
- 随机森林分类器表现出强的性能,平均ROC AUC为0.88,准确度为0.85,回忆率为0.70 (5倍CV).
- 多目标XGBoost模型总体表现良好,但与特定目标模型相比,在离开集上的准确性较低.
结论:
- 针对特定目标的机器学习模型对于预测药物诱导性发作 (DIS) 风险是有效的.
- 这些模型可以帮助优先考虑早期毒性测试的化合物.
- 实施这些预测模型可以显著降低中枢神经系统药物开发中的消耗率.
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